Showing preview only (376K chars total). Download the full file or copy to clipboard to get everything.
Repository: BaptisteBlouin/EventExtractionPapers Branch: master Commit: 6a92eebd6b91 Files: 2 Total size: 366.6 KB Directory structure: gitextract_9gttryg9/ ├── LICENSE └── README.md ================================================ FILE CONTENTS ================================================ ================================================ FILE: LICENSE ================================================ MIT License Copyright (c) 2019 Baptiste Blouin Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ================================================ FILE: README.md ================================================ # Event Extraction papers This repository contains resources for Natural Language Processing (NLP) with a focus on the task of Event Extraction. # Table of Contents <details> <summary><b>Expand Table of Contents</b></summary><blockquote><p align="justify"> - [Pattern Matching](#pattern-matching) - [Machine Learning](#machine-learning) - [Deep Learning](#deep-learning) - [Semi-supervised Learning](#semi-supervised-learning) - [Unsupervised Learning](#unsupervised-learning) - [Event Coreference](#event-coreference) - [Surveys](#surveys) - [Others](#others) - [Linguistics](#linguistics) - [Data](#data) - [Tools and Repos](#tools-and-repos) - [Other lists](#other-lists) </p></blockquote></details> --- ## Pattern matching ### 1993 <details> <summary>1. <a href="https://aaai.org/Papers/AAAI/1993/AAAI93-121.pdf">Automatically Constructing a Dictionary for Information Extraction Tasks</a> by<i> Ellen Riloff </i></summary><blockquote><p align="justify"> Knowledge-based natural language processing systems have achieved good success with certain tasks but they are often criticized because they depend on a domain-specific dictionary that requires a great deal of manual knowledge engineering. This knowledge engineering bottleneck makes knowledge-based NLP systems impractical for real-world applications because they cannot be easily scaled up orported to new domains. In response to this problem, we developed a system called AutoSlog that automatically builds a domain-specific dictionary of concepts for extracting information from text. Using AutoSlog. we constructed a dictionary for the domain of terrorist event descriptions in only 5 person-hours. We then compared the AutoSlog dictionary with a hand-crafted dictionary that was built by two highly skilled graduate students and required approximately 1500 person-hours of effort. We evaluated the two dictionaries using two blind test sets of 100 texts each. Overall, the AutoSlog dictionary achieved 98% of the performance of the hand-crafted dictionary. On the first test set, the Auto-Slog dictionary obtained 96.3% of the perfomlance of the hand-crafted dictionary. On the second test set, the overall scores were virtually indistinguishable with the AutoSlog dictionary achieving 99.7% of the performance of the handcrafted dictionary. </p></blockquote></details> ### 1995 <details> <summary>1. <a href="https://ieeexplore.ieee.org/document/469825">Acquisition of linguistic patterns for knowledge-based information extraction</a> by<i> Jun-Tae Kim ; D.I. Moldovan </i></summary><blockquote><p align="justify"> The paper presents an automatic acquisition of linguistic patterns that can be used for knowledge based information extraction from texts. In knowledge based information extraction, linguistic patterns play a central role in the recognition and classification of input texts. Although the knowledge based approach has been proved effective for information extraction on limited domains, there are difficulties in construction of a large number of domain specific linguistic patterns. Manual creation of patterns is time consuming and error prone, even for a small application domain. To solve the scalability and the portability problem, an automatic acquisition of patterns must be provided. We present the PALKA (Parallel Automatic Linguistic Knowledge Acquisition) system that acquires linguistic patterns from a set of domain specific training texts and their desired outputs. A specialized representation of patterns called FP structures has been defined. Patterns are constructed in the form of FP structures from training texts, and the acquired patterns are tuned further through the generalization of semantic constraints. Inductive learning mechanism is applied in the generalization step. The PALKA system has been used to generate patterns for our information extraction system developed for the fourth Message Understanding Conference (MUC-4). </p></blockquote></details> <details> <summary>2. <a href="https://www.aclweb.org/anthology/W95-0112/">Automatically Acquiring Conceptual Patterns without an Annotated Corpus</a> by<i> Ellen Riloff, Jay Shoen </i></summary><blockquote><p align="justify"> Previous work on automated dictionary construction for information extraction has relied on annotated text corpora. However, annotating a corpus is time-consuming and difficult. We propose that conceptual patterns for information extraction can be acquired automatically using only a preclassified training corpus and no text annotations. We describe a system called AutoSlog-TS, which is a variation of our previous AutoSlog system, that runs exhaustively on an untagged text corpus. Text classification experiments in the MUC-4 terrorism domain show that the AutoSlog-TS dictionary performs comparably to a hand-crafted dictionary, and actually achieves higher precision on one test set. For text classification, AutoSlog-TS requires no manual effort beyond the preclassified training corpus. Additional experiments suggest how a dictionary produced by AutoSlog-TS can be filtered automatically for information extraction tasks. Some manual intervention is still required in this case, but AutoSlog-TS significantly reduces the amount of effort required to create an appropriate training corpus. </p></blockquote></details> <details> <summary>3. <a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.597.3832&rep=rep1&type=pdf">Learning information extraction patterns from examples</a> by<i> Scott B. Huffman </i></summary><blockquote><p align="justify"> A growing population of users want to extract a growing variety of information from on-line texts. Unfortunately, current information extraction systems typically require experts to hand-build dictionaries of extraction patterns for each new type of information to be extracted. This paper presents a system that can learn dictionaries of extraction patterns directly from user-provided examples of texts and events to be extracted from them. The system, called LIEP, learns patterns that recognize relationships between key constituents based on local syntax. Sets of patterns learned by LIEP for a sample extraction task perform nearly at the level of a hand-built dictionary of patterns. </p></blockquote></details> ### 1998 <details> <summary>1. <a href="https://www.semanticscholar.org/paper/Multistrategy-Learning-for-Information-Extraction-Freitag/29c99d263b5e05aae6bb96f004f025dcc9b5caae">Multistrategy Learning for Information Extraction</a> by<i> Dayne Freitag</i></summary><blockquote><p align="justify"> Information extraction IE is the problem of lling out pre de ned structured sum maries from text documents We are in terested in performing IE in non traditional domains where much of the text is often ungrammatical such as electronic bulletin board posts and Web pages We suggest that the best approach is one that takes into ac count many di erent kinds of information and argue for the suitability of a multistrat egy approach We describe learners for IE drawn from three separate machine learning paradigms rote memorization term space text classi cation and relational rule induc tion By building regression models mapping from learner con dence to probability of cor rectness and combining probabilities appro priately it is possible to improve extraction accuracy over that achieved by any individ ual learner We describe three di erent mul tistrategy approaches Experiments on two IE domains a collection of electronic seminar announcements from a university computer science department and a set of newswire ar ticles describing corporate acquisitions from the Reuters collection demonstrate the effectiveness of all three approaches </p></blockquote></details> ### 1999 <details> <summary>1. <a href="https://www.researchgate.net/publication/221603776_Learning_Dictionaries_for_Information_Extraction_by_Multi-Level_Bootstrapping">Learning Dictionaries for Information Extraction by Multi-Level Bootstrapping</a> by<i> Ellen Riloff, Rosie Jones</i></summary><blockquote><p align="justify"> Information extraction systems usually require two dictionaries: a semantic lexicon and a dictionary of extraction patterns for the domain. We present a multilevel bootstrapping algorithm that generates both the semantic lexicon and extraction patterns simultaneously. As input, our technique requires only unannotated training texts and a handful of seed words for a category. We use a mutual bootstrapping technique to alternately select the best extraction pattern for the category and bootstrap its extractions into the semantic lexicon, which is the basis for selecting the next extraction pattern. To make this approach more robust, we add a second level of bootstrapping (metabootstrapping) that retains only the most reliable lexicon entries produced by mutual bootstrapping and then restarts the process. We evaluated this multilevel bootstrapping technique on a collection of corporate web pages and a corpus of terrorism news articles. The algorithm produced high-quality dictionaries for several semantic categories. </p></blockquote></details> ### 2000 <details> <summary>1. <a href="https://www.aclweb.org/anthology/A00-1011/">REES: A Large-Scale Relation and Event Extraction System</a> by<i> Chinatsu Aone, Mila Ramos-Santacruz</i></summary><blockquote><p align="justify"> This paper reports on a large-scale, end-to-end relation and event extraction system. At present, the system extracts a total of 100 types of relations and events, which represents a much wider coverage than is typical of extraction systems. The system consists of three specialized pattem-based tagging modules, a high-precision co-reference resolution module, and a configurable template generation module. We report quantitative evaluation results, analyze the results in detail, and discuss future directions. </p></blockquote></details> <details> <summary>2. <a href="https://www.aclweb.org/anthology/C00-2136/">Automatic Acquisition of Domain Knowledge for Information Extraction</a> by<i> Roman Yangarber, Ralph Grishman, Pasi Tapanainen, Silja Huttunen</i></summary><blockquote><p align="justify"> In developing an Information Extraction (IE) system for a new class of events or relations, one of the major tasks is identifying the many ways in which these events or relations may be expressed in text. This has generally involved the manual analysis and, in some cases, the annotation of large quantities of text involving these events. This paper presents an alternative approach, based on an automatic discovery procedure, ExDisco, which identi es a set of relevant documents and a set of event patterns from un-annotated text, starting from a small set of seed patterns." We evaluate ExDisco by comparing the performance of discovered patterns against that of manually constructed systems on actual extraction tasks. </p></blockquote></details> ### 2001 <details> <summary>1. <a href="https://www.semanticscholar.org/paper/Adaptive-Information-Extraction-from-Text-by-Rule-Ciravegna/436087083293ca8728fb96d2e05c011fff2c7751">Adaptive Information Extraction from Text by Rule Induction and Generalisation</a> by<i> Fabio Ciravegna</i></summary><blockquote><p align="justify"> (LP)2 is a covering algorithm for adaptive Information Extraction from text (IE). It induces symbolic rules that insert SGML tags into texts by learning from examples found in a user-defined tagged corpus. Training is performed in two steps: initially a set of tagging rules is learned; then additional rules are induced to correct mistakes and imprecision in tagging. Induction is performed by bottom-up generalization of examples in the training corpus. Shallow knowledge about Natural Language Processing (NLP) is used in the generalization process. The algorithm has a considerable success story. From a scientific point of view, experiments report excellent results with respect to the current state of the art on two publicly available corpora. From an application point of view, a successful industrial IE tool has been based on (LP)2. Real world applications have been developed and licenses have been released to external companies for building other applications. This paper presents (LP)2, experimental results and applications, and discusses the role of shallow NLP in rule induction. </p></blockquote></details> ### 2002 <details> <summary>1. <a href="https://link.springer.com/chapter/10.1007/3-540-36182-0_30">Event Pattern Discovery from the Stock Market Bulletin</a> by<i> Fang Li, Huanye Sheng, Dongmo Zhang</i></summary><blockquote><p align="justify"> Electronic information grows rapidly as the Internet is widely used in our daily life. In order to identify the exact information for the user query, information extraction is widely researched and investigated. The template, which pertains to events or situations, and contains slots that denote who did what to whom, when, and where, is predefined by a template builder. Therefore, fixed templates are the main obstacles for the information extraction system out of the laboratory. In this paper, a method to automatically discover the event pattern in Chinese from stock market bulletin is introduced. It is based on the tagged corpus and the domain model. The pattern discovery process is independent of the domain model by introducing a link table. The table is the connection between text surface structure and semantic deep structure represented by a domain model. The method can be easily adapted to other domains by changing the link table. </p></blockquote></details> ### 2003 <details> <summary>1. <a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.67.9396&rep=rep1&type=pdf">A System for new event detection</a> by<i> Thorsten Brants, Francine Chen, Ayman Farahat</i></summary><blockquote><p align="justify"> We present a new method and system for performing the New Event Detection task, i.e., in one or multiple streams of news stories, all stories on a previously unseen (new) event are marked. The method is based on an incremental TF-IDF model. Our extensions include: generation of source-specific models, similarity score normalization based on document-specific averages, similarity score normalization based on source-pair specific averages, term reweighting based on inverse event frequencies, and segmentation of the documents. We also report on extensions that did not improve results. The system performs very well on TDT3 and TDT4 test data and scored second in the TDT-2002 evaluation. </p></blockquote></details> <details> <summary>2. <a href="https://www.researchgate.net/publication/220321036_Bottom-Up_Relational_Learning_of_Pattern_Matching_Rules_for_Information_Extraction">Bottom-Up Relational Learning of Pattern Matching Rules for Information Extraction.</a> by<i> Mary Elaine Califf, Raymond J. Mooney</i></summary><blockquote><p align="justify"> Information extraction is a form of shallow text processing that locates a specified set of relevant items in a natural-language document. Systems for this task require significant domain-specific knowledge and are time-consuming and difficult to build by hand, making them a good application for machine learning. We present an algorithm, RAPIER, that uses pairs of sample documents and filled templates to induce pattern-match rules that directly extract fillers for the slots in the template. RAPIER is a bottom-up learning algorithm that incorporates techniques from several inductive logic programming systems. We have implemented the algorithm in a system that allows patterns to have constraints on the words, part-of-speech tags, and semantic classes present in the filler and the surrounding text. We present encouraging experimental results on two domains. </p></blockquote></details> <details> <summary>3. <a href="https://www.aclweb.org/anthology/P03-1029/">An Improved Extraction Pattern Representation Model for Automatic IE Pattern Acquisition</a> by<i> Kiyoshi Sudo, Satoshi Sekine, Ralph Grishman</i></summary><blockquote><p align="justify"> Several approaches have been described for the automatic unsupervised acquisition of patterns for information extraction. Each approach is based on a particular model for the patterns to be acquired, such as a predicate-argument structure or a dependency chain. The effect of these alternative models has not been previously studied. In this paper, we compare the prior models and introduce a new model, the Subtree model, based on arbitrary subtrees of dependency trees. We describe a discovery procedure for this model and demonstrate experimentally an improvement in recall using Subtree patterns. </p></blockquote></details> ### 2005 <details> <summary>1. <a href="https://www.aaai.org/Papers/Workshops/2006/WS-06-07/WS06-07-004.pdf">Automatic event and relation detection with seeds of varying complexity</a> by<i> Feiyu Xu, Hans Uszkoreit and Hong Li</i></summary><blockquote><p align="justify"> In this paper, we present an approach for automatically detecting events in natural language texts by learning patterns that signal the mentioning of such events. We construe the relevant event types as relations and start with aset of seeds consisting of representative event instances thath appen to be known and also to be mentioned frequently in easily available training data. Methods have been developed for the automatic identification of event extents andevent triggers. We have learned patterns for a particular domain, i.e., prize award events. Currently we are systematically investigating the criteria for selecting the most effective patterns for the detection of events in sentences and paragraphs. Although the systematic investigation is still under way, we can already report on first very promising results of the method for learning of patterns and for using these patterns in event detection. </p></blockquote></details> <details> <summary>2. <a href="https://www.semanticscholar.org/paper/A-Semantic-Approach-to-IE-Pattern-Induction-Stevenson-Greenwood/f30b903047284e8a253b2da38530b99b6db13317">A Semantic Approach to IE Pattern Induction</a> by<i> Mark Stevenson, Mark A. Greenwood</i></summary><blockquote><p align="justify"> This paper presents a novel algorithm for the acquisition of Information Extraction patterns. The approach makes the assumption that useful patterns will have similar meanings to those already identified as relevant. Patterns are compared using a variation of the standard vector space model in which information from an ontology is used to capture semantic similarity. Evaluation shows this algorithm performs well when compared with a previously reported document-centric approach. </p></blockquote></details> ### 2008 <details> <summary>1. <a href="https://link.springer.com/chapter/10.1007/978-3-540-69858-6_21">Real-Time News Event Extraction for Global Crisis Monitoring</a> by<i> Hristo Tanev, Jakub Piskorski, Martin Atkinson</i></summary><blockquote><p align="justify"> This paper presents a real-time news event extraction system developed by the Joint Research Centre of the European Commission. It is capable of accurately and efficiently extracting violent and disaster events from online news without using much linguistic sophistication. In particular, in our linguistically relatively lightweight approach to event extraction, clustered news have been heavily exploited at various stages of processing. The paper describes the system’s architecture, news geo-tagging, automatic pattern learning, pattern specification language, information aggregation, the issues of integrating event information in a global crisis monitoring system and new experimental evaluation. </p></blockquote></details> ### 2009 <details> <summary>1. <a href="https://www.aclweb.org/anthology/W09-1405/">Biomedical event extraction without training data</a> by<i> Andreas Vlachos, Paula Buttery, Diarmuid Ó Séaghdha, Ted Briscoe </i></summary><blockquote><p align="justify"> We describe our system for the BioNLP 2009 event detection task. It is designed to be as domain-independent and unsupervised as possible. Nevertheless, the precisions achieved for single theme event classes range from 75% to 92%, while maintaining reasonable recall. The overall F-scores achieved were 36.44% and 30.80% on the development and the test sets respectively. </p></blockquote></details> <details> <summary>2. <a href="https://www.aclweb.org/anthology/W09-1418/">Syntactic dependency based heuristics for biological event extraction</a> by<i> Halil Kilicoglu, Sabine Bergler</i></summary><blockquote><p align="justify"> We explore a rule-based methodology for the BioNLP'09 Shared Task on Event Extraction, using dependency parsing as the underlying principle for extracting and characterizing events. We approach the speculation and negation detection task with the same principle. Evaluation results demonstrate the utility of this syntax-based approach and point out some shortcomings that need to be addressed in future work. </p></blockquote></details> ### 2010 <details> <summary>1. <a href="https://personal.eur.nl/frasincar/papers/IJWET2010/ijwet2010.pdf">Semi-Automatic Financial Events Discovery Based on Lexico-Semantic Patterns</a> by<i> Jethro Borsje, Frederik Hogenboom, Flavius Frasincar </i></summary><blockquote><p align="justify"> Due to the market sensitivity to emerging news, investors on financial markets need to continuously monitor financial events when deciding on buying and selling equities. We propose the use of lexico-semantic patterns for financial event extraction from RSS news feeds. These patterns use financial ontologies, leveraging the commonly used lexico-syntactic patterns to a higher abstraction level, thereby enabling lexico-semantic patterns to identify more and more precisely events than lexico-syntactic patterns from text. We have developed rules based on lexico-semantic patterns used to find events, and semantic actions that allow for updating the domain ontology with the effects of the discovered events. Both the lexico-semantic patterns and the semantic actions make use of the triple paradigm that fosters their easy construction and understanding by the user. Based on precision, recall, and F1 measures, we show the effectiveness of the proposed approach. </p></blockquote></details> <details> <summary>2. <a href="https://www.aclweb.org/anthology/C10-1077/">Filtered Ranking for Bootstrapping in Event Extraction</a> by<i> Shasha Liao, Ralph Grishman</i></summary><blockquote><p align="justify"> Several researchers have proposed semi-supervised learning methods for adapting event extraction systems to new event types. This paper investigates two kinds of bootstrapping methods used for event extraction: the document-centric and similarity-centric approaches, and proposes a filtered ranking method that combines the advantages of the two. We use a range of extraction tasks to compare the generality of this method to previous work. We analyze the results using two evaluation metrics and observe the effect of different training corpora. Experiments show that our new ranking method not only achieves higher performance on different evaluation metrics, but also is more stable across different bootstrapping corpora. </p></blockquote></details> ### 2011 <details> <summary>1. <a href="https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1467-8640.2011.00401.x">Effective bio-event extraction using trigger words and syntactic dependencies</a> by<i> Halil Kilicoglu, Sabine Bergler </i></summary><blockquote><p align="justify"> The scientific literature is the main source for comprehensive, up-to-date biological knowledge. Automatic extraction of this knowledge facilitates core biological tasks, such as database curation and knowledge discovery. We present here a linguistically inspired, rule-based and syntax-driven methodology for biological event extraction. We rely on a dictionary of trigger words to detect and characterize event expressions and syntactic dependency based heuristics to extract their event arguments. We refine and extend our prior work to recognize speculated and negated events. We show that heuristics based on syntactic dependencies, used to identify event arguments, extend naturally to also identify speculation and negation scope. In the BioNLP’09 Shared Task on Event Extraction, our system placed third in the Core Event Extraction Task (F-score of 0.4462), and first in the Speculation and Negation Task (F-score of 0.4252). Of particular interest is the extraction of complex regulatory events, where it scored second place. Our system significantly outperformed other participating systems in detecting speculation and negation. These results demonstrate the utility of a syntax-driven approach. In this article, we also report on our more recent work on supervised learning of event trigger expressions and discuss event annotation issues, based on our corpus analysis. </p></blockquote></details> ### 2012 <details> <summary>1. <a href="https://link.springer.com/chapter/10.1007%2F978-3-642-33185-5_10">Ontology-Based Information and Event Extraction for Business Intelligence</a> by<i> Ernest Arendarenko, Tuomo Kakkonen</i></summary><blockquote><p align="justify"> We would like to introduce BEECON, an information and event extraction system for business intelligence. This is the first ontology-based system for business documents analysis that is able to detect 41 different types of business events from unstructured sources of information. The described system is intended to enhance business intelligence efficiency by automatically extracting relevant content such as business entities and events. In order to achieve it, we use natural language processing techniques, pattern recognition algorithms and hand-written detection rules. In our test set consisting of 190 documents with 550 events, the system achieved 95% precision and 67% recall in detecting all supported business event types from newspaper texts. </p></blockquote></details> <details> <summary>2. <a href="https://ieeexplore.ieee.org/document/6299414">A Real -- Time News Event Extraction Framework for Vietnamese</a> by<i> Mai-Vu Tran , Minh-Hoang Nguyen , Sy-Quan Nguyen , Minh-Tien Nguyen, Xuan-Hieu Phan</i></summary><blockquote><p align="justify"> Event Extraction is a complex and interesting topic in Information Extraction that includes event extraction methods from free text or web data. The result of event extraction systems can be used in several fields such as risk analysis systems, online monitoring systems or decide support tools. In this paper, we introduce a method that combines lexico -- semantic and machine learning to extract event from Vietnamese news. Furthermore, we concentrate to describe event online monitoring system named VnLoc based on the method that was proposed above to extract event in Vietnamese language. Besides, in experiment phase, we have evaluated this method based on precision, recall and F1 measure. At this time of experiment, we on investigated on three types of event: FIRE, CRIME and TRANSPORT ACCIDENT. </p></blockquote></details> <details> <summary>3. <a href="https://www.aclweb.org/anthology/E12-1029/">Bootstrapped Training of Event Extraction Classifiers</a> by<i> Ruihong Huang, Ellen Riloff</i></summary><blockquote><p align="justify"> Most event extraction systems are trained with supervised learning and rely on a collection of annotated documents. Due to the domain-specificity of this task, event extraction systems must be retrained with new annotated data for each domain. In this paper, we propose a bootstrapping solution for event role filler extraction that requires minimal human supervision. We aim to rapidly train a state-of-the-art event extraction system using a small set of "seed nouns" for each event role, a collection of relevant (in-domain) and irrelevant (out-of-domain) texts, and a semantic dictionary. The experimental results show that the bootstrapped system outperforms previous weakly supervised event extraction systems on the MUC-4 data set, and achieves performance levels comparable to supervised training with 700 manually annotated documents. </p></blockquote></details> ### 2015 <details> <summary>1. <a href="https://www.aclweb.org/anthology/P15-4022/">A Domain-independent Rule-based Framework for Event Extraction</a> by<i> Marco A. Valenzuela-Escárcega, Gus Hahn-Powell, Mihai Surdeanu, Thomas Hicks</i></summary><blockquote><p align="justify"> We describe the design, development, and API of ODIN (Open Domain INformer), a domain- independent, rule-based event extraction (EE) framework. The proposed EE approach is: simple (most events are captured with simple lexico-syntactic patterns), powerful (the language can capture complex constructs, such as events taking other events as arguments, and regular expressions over syntactic graphs), robust (to recover from syntactic parsing errors, syntactic patterns can be freely mixed with surface, token-based patterns), and fast (the runtime environment processes 110 sentences/second in a real-world domain with a grammar of over 200 rules). We used this framework to develop a grammar for the bio-chemical domain, which approached human performance. Our EE framework is accompanied by a web-based user interface for the rapid development of event grammars and visualization of matches. The ODIN framework and the domain-specific grammars are available as open-source code. </p></blockquote></details> <details> <summary>2. <a href="https://dl.acm.org/citation.cfm?doid=3008658.2994600">Minimally Supervised Chinese Event Extraction from Multiple Views</a> by<i> Peifeng Li , Guodong Zhou, Qiaoming Zhu</i></summary><blockquote><p align="justify"> Although several semi-supervised learning models have been proposed for English event extraction, there are few successful stories in Chinese due to its special characteristics. In this article, we propose a novel minimally supervised model for Chinese event extraction from multiple views. Besides the traditional pattern similarity view (PSV), a semantic relationship view (SRV) is introduced to capture the relevant event mentions from relevant documents. Moreover, a morphological structure view (MSV) is incorporated to both infer more positive patterns and help filter negative patterns via morphological structure similarity. An evaluation of the ACE 2005 Chinese corpus shows that our minimally supervised model significantly outperforms several strong baselines. </p></blockquote></details> <details> <summary>3. <a href="https://www.aclweb.org/anthology/R15-1010/">Improving Event Detection with Active Learning</a> by<i> Kai Cao, Xiang Li, Miao Fan, Ralph Grishman</i></summary><blockquote><p align="justify"> Event Detection (ED), one aspect of Information Extraction, involves identifying instances of specified types of events in text. Much of the research on ED has been based on the specifications of the 2005 ACE [Automatic Content Extraction] event task 1 , and the associated annotated corpus. However, as the event instances in the ACE corpus are not evenly distributed, some frequent expressions involving ACE events do not appear in the training data, adversely affecting performance. In this paper, we demonstrate the effectiveness of a Pattern Expansion technique to import frequent patterns extracted from external corpora to boost ED performance. The experimental results show that our pattern-based system with the expanded patterns can achieve 70.4% (with 1.6% absolute improvement) F-measure over the baseline, an advance over current state-of-the-art systems. </p></blockquote></details> ### 2018 <details> <summary>1. <a href="https://pdfs.semanticscholar.org/dc04/f814fb210edf62a6237c52eb88ac98a5b732.pdf">Including new patterns to improve event extraction systems</a> by<i> Kai Cao,Xiang Li,Weicheng Ma,Ralph Grishman</i></summary><blockquote><p align="justify"> Event Extraction (EE) is a challenging Information Extraction task which aims to discover event triggers of specific types along with their arguments. Most recent research on Event Extraction relies on pattern-based or feature-based approaches, trained on annotated corpora, to recognize combi- nations of event triggers, arguments, and other contextual in- formation. However, as the event instances in the ACE corpus are not evenly distributed, some frequent expressions involving ACE event triggers do not appear in the training data, adversely affecting the performance. In this paper, we demon- strate the effectiveness of systematically importing expert-level patterns from TABARI to boost EE performance. The experimental results demonstrate that our pattern-based sys- tem with the expanded patterns can achieve 69.8% (with 1.9% absolute improvement) F-measure over the baseline, an advance over current state-of-the-art systems. </p></blockquote></details> <details> <summary>2. <a href="https://www.researchgate.net/publication/329735770_Rule_based_Event_Extraction_System_from_Newswires_and_Social_Media_Text_in_Indian_Languages_EventXtract-IL_for_English_and_Hindi_Data">Rule Based Event Extraction System from Newswires and Social Media Text in Indian Languages (EventXtract-IL) for English and Hindi data</a> by<i> Anita Saroj, Rajesh kumar Munodtiya, and Sukomal Pal </i></summary><blockquote><p align="justify"> Due to today’s information overload, the user is particularly finding it difficult to access the right information through the World Wide Web. The situation becomes worse when this information is in multiple languages. In this paper we present a model for information extraction. Our model mainly works on the concept of speech tagging and named entity recognization. We represent each word with the POS tag and the entity identified for that term. We assume that the event exists in the first line of the document. If we do not find it in the first line, then we take the help of emotion analysis. If it has negative polarity, then it is associated with an unexpected event which has negative meaning. We use NLTK for emotion analysis. </p></blockquote></details> ## Machine learning [:arrow_up:](#table-of-contents) ### 2006 <details> <summary>1. <a href="https://www.aclweb.org/anthology/W06-0901/">The stages of event extraction</a> by<i> David Ahn</i></summary><blockquote><p align="justify"> Event detection and recognition is a complex task consisting of multiple sub-tasks of varying difficulty. In this paper, we present a simple, modular approach to event extraction that allows us to experiment with a variety of machine learning methods for these sub-tasks, as well as to evaluate the impact on performance these sub-tasks have on the overall task. </p></blockquote></details> ### 2007 <details> <summary>1. <a href="https://link.springer.com/chapter/10.1007/978-3-540-72035-5_22">Extracting Violent Events From On-Line News for Ontology Population</a> by<i> Jakub Piskorski, Hristo Tanev, Pinar Oezden Wennerberg</i></summary><blockquote><p align="justify"> This paper presents nexus, an event extraction system, developed at the Joint Research Center of the European Commission utilized for populating violent incident knowledge bases. It automatically extracts security-related facts from on-line news articles. In particular, the paper focuses on a novel bootstrapping algorithm for weakly supervised acquisition of extraction patterns from clustered news, cluster-level information fusion and pattern specification language. Finally, a preliminary evaluation of nexus on real-world data is given which revealed acceptable precision and a strong application potential. </p></blockquote></details> ### 2008 <details> <summary>1. <a href="https://www.researchgate.net/publication/255646580_Research_on_Chinese_Event_Extraction">Research on Chinese Event Extraction</a> by<i> Yanyan Zhao, Bing Qin, Wanxiang Che, Ting Liu</i></summary><blockquote><p align="justify"> Event Extraction is an important research point in the area of Information Extraction. This paper makes an intensive study of the two stages of Chinese event extraction, namely event type recognition and event argument recognition. A novel method combining event trigger expansion and a binary classifier is presented in the step of event type recognition while in the step of argument recognition, one with multi-class classification based on maximum entropy is introduced. The above methods solved the data unbalanced problem in training model and the data sparseness problem brought by the small set of training data effectively, and finally our event extraction system achieved a better performance. </p></blockquote></details> <details> <summary>2. <a href="https://www.aclweb.org/anthology/P08-1030/">Refining Event Extraction through Cross-Document Inference</a> by<i> Heng Ji, Ralph Grishman</i></summary><blockquote><p align="justify"> We apply the hypothesis of "One Sense Per Discourse" (Yarowsky, 1995) to information extraction (IE), and extend the scope of "discourse" from one single document to a cluster of topically-related documents. We employ a similar approach to propagate consistent event arguments across sentences and documents. Combining global evidence from related documents with local decisions, we design a simple scheme to conduct cross-document inference for improving the ACE event extraction task 1 . Without using any additional labeled data this new approach obtained 7.6% higher F-Measure in trigger labeling and 6% higher F-Measure in argument labeling over a state-of-the-art IE system which extracts events independently for each sentence. </p></blockquote></details> ### 2009 <details> <summary>1. <a href="https://www.aclweb.org/anthology/W09-1402/">Extracting Complex Biological Events with Rich Graph-Based Feature Sets</a> by<i> Jari Björne, Juho Heimonen, Filip Ginter, Antti Airola, Tapio Pahikkala, Tapio Salakoski</i></summary><blockquote><p align="justify"> We describe a system for extracting complex events among genes and proteins from biomedical literature, developed in context of the BioNLP’09 Shared Task on Event Extraction. For each event, the system extracts its text trigger, class, and arguments. In contrast to the approaches prevailing prior to the shared task, events can be arguments of other events, resulting in a nested structure that better captures the underlying biological statements. We divide the task into independent steps which we approach as machine learning problems. We define a wide array of features and in particular make extensive use of dependency parse graphs. A rule‐based postprocessing step is used to refine the output in accordance with the restrictions of the extraction task. In the shared task evaluation, the system achieved an F‐score of 51.95% on the primary task, the best performance among the participants. Currently, with modifications and improvements described in this article, the system achieves 52.86% F‐score on Task 1, the primary task, improving on its original performance. In addition, we extend the system also to Tasks 2 and 3, gaining F‐scores of 51.28% and 50.18%, respectively. The system thus addresses the BioNLP’09 Shared Task in its entirety and achieves the best performance on all three subtasks. </p></blockquote></details> <details> <summary>2. <a href="https://pdfs.semanticscholar.org/b6ce/13412a9cadb6c57f1349ad389affbdea2321.pdf">Language Specific Issue and Feature Exploration in Chinese Event Extraction</a> by<i> Zheng Chen, Heng Ji</i></summary><blockquote><p align="justify"> In this paper, we present a Chinese event extraction system. We point out a language spe- cific issue in Chinese trigger labeling, and then commit to discussing the contributions of lexical, syntactic and semantic features applied in trigger labeling and argument labeling. As a result, we achieved competitive performance, specifically, F-measure of 59.9 in trigger labeling and F-measure of 43.8 in argument labeling. </p></blockquote></details> <details> <summary>3. <a href="https://www.aclweb.org/anthology/W09-1406/">A Markov Logic Approach to Bio-Molecular Event Extraction</a> by<i> Sebastian Riedel, Hong-Woo Chun, Toshihisa Takagi, Jun’ichi Tsujii </i></summary><blockquote><p align="justify"> In this paper we describe our entry to the BioNLP 2009 Shared Task regarding biomolecular event extraction. Our work can be described by three design decisions: (1) instead of building a pipeline using local classifier technology, we design and learn a joint probabilistic model over events in a sentence; (2) instead of developing specific inference and learning algorithms for our joint model, we apply Markov Logic, a general purpose Statistical Relation Learning language, for this task; (3) we represent events as relational structures over the tokens of a sentence, as opposed to structures that explicitly mention abstract event entities. Our results are competitive: we achieve the 4th best scores for task 1 (in close range to the 3rd place) and the best results for task 2 with a 13 percent point margin. </p></blockquote></details> ### 2010 <details> <summary>1. <a href="https://www.worldscientific.com/doi/abs/10.1142/S0219720010004586">Event Extraction with Complex Event Classification Using Rich Features</a> by<i> MAKOTO MIWA, RUNE SÆTRE, JIN-DONG KIM and JUN'ICHI TSUJII </i></summary><blockquote><p align="justify"> Biomedical Natural Language Processing (BioNLP) attempts to capture biomedical phenomena from texts by extracting relations between biomedical entities (i.e. proteins andgenes). Traditionally, only binary relations have been extracted from large numbers of published papers. Recently, more complex relations (biomolecular events) have also been extracted. Such events may include several entities or other relations. To evaluate the performance of the text mining systems, several shared task challenges have been arranged for the BioNLP community. With a common and consistent task setting, theBioNLP’09 shared task evaluated complex biomolecular events such as binding and regulation. Finding these events automatically is important in order to improve biomedical event extraction systems. In the present paper, we propose an automatic event extraction system, which contains a model for complex events, by solving a classification problem with rich features. The main contributions of the present paper are: (1) the proposal of an effective bio-event detection method using machine learning, (2) provision of a high-performance event extraction system, and (3) the execution of a quantitative error analysis. The proposed complex (binding and regulation) event detector outperforms the best system from the BioNLP’09 shared task challenge. </p></blockquote></details> <details> <summary>2. <a href="https://academic.oup.com/bioinformatics/article/26/12/i382/282442">Complex event extraction at PubMed scale</a> by<i> Björne J, Ginter F, Pyysalo S, Tsujii J, Salakoski T.</i></summary><blockquote><p align="justify"> There has recently been a notable shift in biomedical information extraction (IE) from relation models toward the more expressive event model, facilitated by the maturation of basic tools for biomedical text analysis and the availability of manually annotated resources. The event model allows detailed representation of complex natural language statements and can support a number of advanced text mining applications ranging from semantic search to pathway extraction. A recent collaborative evaluation demonstrated the potential of event extraction systems, yet there have so far been no studies of the generalization ability of the systems nor the feasibility of large-scale extraction. This study considers event-based IE at PubMed scale. We introduce a system combining publicly available, state-of-the-art methods for domain parsing, named entity recognition and event extraction, and test the system on a representative 1% sample of all PubMed citations. We present the first evaluation of the generalization performance of event extraction systems to this scale and show that despite its computational complexity, event extraction from the entire PubMed is feasible. We further illustrate the value of the extraction approach through a number of analyses of the extracted information. The event detection system and extracted data are open source licensed and available at http://bionlp.utu.fi/. </p></blockquote></details> <details> <summary>3. <a href="https://www.aclweb.org/anthology/P10-1081/">Using Document Level Cross-Event Inference to Improve Event Extraction</a> by<i> Shasha Liao, Ralph Grishman</i></summary><blockquote><p align="justify"> Event extraction is a particularly challenging type of information extraction (IE). Most current event extraction systems rely on local information at the phrase or sentence level. However, this local context may be insufficient to resolve ambiguities in identifying particular types of events; information from a wider scope can serve to resolve some of these ambiguities. In this paper, we use document level information to improve the performance of ACE event extraction. In contrast to previous work, we do not limit ourselves to information about events of the same type, but rather use information about other types of events to make predictions or resolve ambiguities regarding a given event. We learn such relationships from the training corpus and use them to help predict the occurrence of events and event arguments in a text. Experiments show that we can get 9.0% (absolute) gain in trigger (event) classification, and more than 8% gain for argument (role) classification in ACE event extraction. </p></blockquote></details> ### 2011 <details> <summary>1. <a href="https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-12-S2-S4">Word sense disambiguation for event trigger word detection in biomedicine</a> by<i> David Martinez , Timothy Baldwin </i></summary><blockquote><p align="justify"> This paper describes a method for detecting event trigger words in biomedical text based on a word sense disambiguation (WSD) approach. We first investigate the applicability of existing WSD techniques to trigger word disambiguation in the BioNLP 2009 shared task data, and find that we are able to outperform a traditional CRF-based approach for certain word types. On the basis of this finding, we combine the WSD approach with the CRF, and obtain significant improvements over the standalone CRF, gaining particularly in recall. </p></blockquote></details> <details> <summary>2. <a href="https://www.aclweb.org/anthology/P11-1113/">Using Cross-Entity Inference to Improve Event Extraction</a> by<i> Yu Hong, Jianfeng Zhang, Bin Ma, Jianmin Yao, Guodong Zhou, Qiaoming Zhu</i></summary><blockquote><p align="justify"> Event extraction is the task of detecting certain specified types of events that are mentioned in the source language data. The state-of-the-art research on the task is transductive inference (e.g. cross-event inference). In this paper, we propose a new method of event extraction by well using cross-entity inference. In contrast to previous inference methods, we regard entity-type consistency as key feature to predict event mentions. We adopt this inference method to improve the traditional sentence-level event extraction system. Experiments show that we can get 8.6% gain in trigger (event) identification, and more than 11.8% gain for argument (role) classification in ACE event extraction. </p></blockquote></details> <details> <summary>3. <a href="https://www.aclweb.org/anthology/R11-1002/">Acquiring Topic Features to improve Event Extraction: in Pre-selected and Balanced Collections</a> by<i> Shasha Liao, Ralph Grishman</i></summary><blockquote><p align="justify"> Event extraction is a particularly challenging type of information extraction (IE) that may require inferences from the whole article. However, most current event extraction systems rely on local information at the phrase or sentence level, and do not consider the article as a whole, thus limiting extraction performance. Moreover, most annotated corpora are artificially enriched to include enough positive samples of the events of interest; event identification on a more balanced collection, such as unfiltered newswire, may perform much worse. In this paper, we investigate the use of unsupervised topic models to extract topic features to improve event extraction both on test data similar to training data, and on more balanced collections. We compare this unsupervised approach to a supervised multi-label text classifier, and show that unsupervised topic modeling can get better results for both collections, and especially for a more balanced collection. We show that the unsupervised topic model can improve trigger, argument and role labeling by 3.5%, 6.9% and 6% respectively on a pre-selected corpus, and by 16.8%, 12.5% and 12.7% on a balanced corpus. </p></blockquote></details> <details> <summary>4. <a href="https://www.aclweb.org/anthology/D11-1001/">Fast and Robust Joint Models for Biomedical Event Extraction</a> by<i> Sebastian Riedel, Andrew McCallum</i></summary><blockquote><p align="justify"> Extracting biomedical events from literature has attracted much recent attention. The best-performing systems so far have been pipelines of simple subtask-specific local classifiers. A natural drawback of such approaches are cascading errors introduced in early stages of the pipeline. We present three joint models of increasing complexity designed to overcome this problem. The first model performs joint trigger and argument extraction, and lends itself to a simple, efficient and exact inference algorithm. The second model captures correlations between events, while the third model ensures consistency between arguments of the same event. Inference in these models is kept tractable through dual decomposition. The first two models outperform the previous best joint approaches and are very competitive with respect to the current state-of-the-art. The third model yields the best results reported so far on the BioNLP 2009 shared task, the BioNLP 2011 Genia task and the BioNLP 2011 Infectious Diseases task. </p></blockquote></details> <details> <summary>5. <a href="https://jbiomedsem.biomedcentral.com/articles/10.1186/2041-1480-2-S5-S6">Coreference based event-argument relation extraction on biomedical text</a> by<i> Katsumasa Yoshikawa, Sebastian Riedel, Tsutomu Hirao, Masayuki Asahara & Yuji Matsumoto</i></summary><blockquote><p align="justify"> This paper presents a new approach to exploit coreference information for extracting event-argument (E-A) relations from biomedical documents. This approach has two advantages: (1) it can extract a large number of valuable E-A relations based on the concept of salience in discourse; (2) it enables us to identify E-A relations over sentence boundaries (cross-links) using transitivity of coreference relations. We propose two coreference-based models: a pipeline based on Support Vector Machine (SVM) classifiers, and a joint Markov Logic Network (MLN). We show the effectiveness of these models on a biomedical event corpus. Both models outperform the systems that do not use coreference information. When the two proposed models are compared to each other, joint MLN outperforms pipeline SVM with gold coreference information. </p></blockquote></details> <details> <summary>6. <a href="https://www.aclweb.org/anthology/W11-1805/">Biomedical Event Extraction from Abstracts and Full Papers using Search-based Structured Prediction</a> by<i> Andreas Vlachos, Mark Craven</i></summary><blockquote><p align="justify"> Biomedical event extraction has attracted substantial attention as it can assist researchers in understanding the plethora of interactions among genes that are described in publications in molecular biology. While most recent work has focused on abstracts, the BioNLP 2011 shared task evaluated the submitted systems on both abstracts and full papers. In this article, we describe our submission to the shared task which decomposes event extraction into a set of classification tasks that can be learned either independently or jointly using the search-based structured prediction framework. Our intention is to explore how these two learning paradigms compare in the context of the shared task. We report that models learned using search-based structured prediction exceed the accuracy of independently learned classifiers by 8.3 points in F-score, with the gains being more pronounced on the more complex Regulation events (13.23 points). Furthermore, we show how the trade-off between recall and precision can be adjusted in both learning paradigms and that search-based structured prediction achieves better recall at all precision points. Finally, we report on experiments with a simple domain-adaptation method, resulting in the second-best performance achieved by a single system. We demonstrate that joint inference using the search-based structured prediction framework can achieve better performance than independently learned classifiers, thus demonstrating the potential of this learning paradigm for event extraction and other similarly complex information-extraction tasks. </p></blockquote></details> <details> <summary>7. <a href="https://www.sciencedirect.com/science/article/pii/S0933365711001060?via%3Dihub">Biomedical events extraction using the hidden vector state model</a> by<i> Deyu Zhou, Yulan He</i></summary><blockquote><p align="justify"> Biomedical events extraction concerns about events describing changes on the state of bio-molecules from literature. Comparing to the protein-protein interactions (PPIs) extraction task which often only involves the extraction of binary relations between two proteins, biomedical events extraction is much harder since it needs to deal with complex events consisting of embedded or hierarchical relations among proteins, events, and their textual triggers. In this paper, we propose an information extraction system based on the hidden vector state (HVS) model, called HVS-BioEvent, for biomedical events extraction, and investigate its capability in extracting complex events. HVS has been previously employed for extracting PPIs. In HVS-BioEvent, we propose an automated way to generate abstract annotations for HVS training and further propose novel machine learning approaches for event trigger words identification, and for biomedical events extraction from the HVS parse results. Our proposed system achieves an F-score of 49.57% on the corpus used in the BioNLP'09 shared task, which is only 2.38% lower than the best performing system by UTurku in the BioNLP'09 shared task. Nevertheless, HVS-BioEvent outperforms UTurku's system on complex events extraction with 36.57% vs. 30.52% being achieved for extracting regulation events, and 40.61% vs. 38.99% for negative regulation events. The results suggest that the HVS model with the hierarchical hidden state structure is indeed more suitable for complex event extraction since it could naturally model embedded structural context in sentences. </p></blockquote></details> <details> <summary>8. <a href="https://www.aclweb.org/anthology/P11-1163/">Event Extraction as Dependency Parsing</a> by<i> David McClosky, Mihai Surdeanu, Christopher Manning</i></summary><blockquote><p align="justify"> Nested event structures are a common occurrence in both open domain and domain specific extraction tasks, e.g., a "crime" event can cause a "investigation" event, which can lead to an "arrest" event. However, most current approaches address event extraction with highly local models that extract each event and argument independently. We propose a simple approach for the extraction of such structures by taking the tree of event-argument relations and using it directly as the representation in a reranking dependency parser. This provides a simple framework that captures global properties of both nested and flat event structures. We explore a rich feature space that models both the events to be parsed and context from the original supporting text. Our approach obtains competitive results in the extraction of biomedical events from the BioNLP'09 shared task with a F1 score of 53.5% in development and 48.6% in testing. </p></blockquote></details> <details> <summary>9. <a href="https://www.aclweb.org/anthology/W11-1807/">Robust Biomedical Event Extraction with Dual Decomposition and Minimal Domain Adaptation</a> by<i> Sebastian Riedel, Andrew McCallum</i></summary><blockquote><p align="justify"> We present a joint model for biomedical event extraction and apply it to four tracks of the BioNLP 2011 Shared Task. Our model decomposes into three sub-models that concern (a) event triggers and outgoing arguments, (b) event triggers and incoming arguments and (c) protein-protein bindings. For efficient decoding we employ dual decomposition. Our results are very competitive: With minimal adaptation of our model we come in second for two of the tasks---right behind a version of the system presented here that includes predictions of the Stanford event extractor as features. We also show that for the Infectious Diseases task using data from the Genia track is a very effective way to improve accuracy. </p></blockquote></details> <details> <summary>10. <a href="https://www.aaai.org/Papers/AAAI/2002/AAAI02-118.pdf">A maximum entropy approach to information extraction from semi-structured and free text</a> by<i> Hai Leong Chieu, Hwee Tou Ng</i></summary><blockquote><p align="justify"> In this paper, we present a classification-based approach towards single-slot as well as multi-slot information extraction (IE). For single-slot IE, we worked on the domain of Seminar Announcements, where each document contains information on only one seminar. For multi-slot IE, we worked on the domain of Management Succession. For this domain, we restrict ourselves to extracting information sentence by sentence, in the same way as (Soderland 1999). Each sentence can contain information on several management succession events. By using a classification approach based on a maximum entropy framework, our system achieves higher accuracy than the best previously published results in both domains. </p></blockquote></details> ### 2012 <details> <summary>1. <a href="https://academic.oup.com/bioinformatics/article/28/13/1759/234417">Boosting automatic event extraction from the literature using domain adaptation and coreference resolution</a> by<i> Makoto Miwa, Paul Thompson, Sophia Ananiadou</i></summary><blockquote><p align="justify"> In recent years, several biomedical event extraction (EE) systems have been developed. However, the nature of the annotated training corpora, as well as the training process itself, can limit the performance levels of the trained EE systems. In particular, most event-annotated corpora do not deal adequately with coreference. This impacts on the trained systems' ability to recognize biomedical entities, thus affecting their performance in extracting events accurately. Additionally, the fact that most EE systems are trained on a single annotated corpus further restricts their coverage. We have enhanced our existing EE system, EventMine, in two ways. First, we developed a new coreference resolution (CR) system and integrated it with EventMine. The standalone performance of our CR system in resolving anaphoric references to proteins is considerably higher than the best ranked system in the COREF subtask of the BioNLP'11 Shared Task. Secondly, the improved EventMine incorporates domain adaptation (DA) methods, which extend EE coverage by allowing several different annotated corpora to be used during training. Combined with a novel set of methods to increase the generality and efficiency of EventMine, the integration of both CR and DA have resulted in significant improvements in EE, ranging between 0.5% and 3.4% F-Score. The enhanced EventMine outperforms the highest ranked systems from the BioNLP'09 shared task, and from the GENIA and Infectious Diseases subtasks of the BioNLP'11 shared task. The improved version of EventMine, incorporating the CR system and DA methods, is available at: http://www.nactem.ac.uk/EventMine/. </p></blockquote></details> <details> <summary>2. <a href="https://www.aclweb.org/anthology/D12-1092/">Employing Compositional Semantics and Discourse Consistency in Chinese Event Extraction</a> by<i> Peifeng Li, Guodong Zhou, Qiaoming Zhu, Libin Hou </i></summary><blockquote><p align="justify"> Current Chinese event extraction systems suffer much from two problems in trigger identification: unknown triggers and word segmentation errors to known triggers. To resolve these problems, this paper proposes two novel inference mechanisms to explore special characteristics in Chinese via compositional semantics inside Chinese triggers and discourse consistency between Chinese trigger mentions. Evaluation on the ACE 2005 Chinese corpus justifies the effectiveness of our approach over a strong baseline. </p></blockquote></details> <details> <summary>3. <a href="https://www.aaai.org/ocs/index.php/AAAI/AAAI12/paper/viewPaper/5113">Modeling Textual Cohesion for Event Extraction</a> by<i> Ruihong Huang, Ellen Riloff</i></summary><blockquote><p align="justify"> Event extraction systems typically locate the role fillers for an event by analyzing sentences in isolation and identifying each role filler independently of the others. We argue that more accurate event extraction requires a view of the larger context to decide whether an entity is related to a relevant event. We propose a bottom-up approach to event extraction that initially identifies candidate role fillers independently and then uses that information as well as discourse properties to model textual cohesion. The novel component of the architecture is a sequentially structured sentence classifier that identifies event-related story contexts. The sentence classifier uses lexical associations and discourse relations across sentences, as well as domain-specific distributions of candidate role fillers within and across sentences. This approach yields state-of-the-art performance on the MUC-4 data set, achieving substantially higher precision than previous systems. </p></blockquote></details> <details> <summary>4. <a href="https://www.aclweb.org/anthology/C12-1100/">Joint Modeling of Trigger Identification and Event Type Determination in Chinese Event Extraction</a> by<i> Peifeng Li, Qiaoming Zhu, Hongjun Diao, Guodong Zhou</i></summary><blockquote><p align="justify"> Currently, Chinese event extraction systems suffer much from the low quality of annotated event corpora and the high ratio of pseudo trigger mentions to true ones. To resolve these two issues, this paper proposes a joint model of trigger identification and event type determination. Besides, several trigger filtering schemas are introduced to filter out those pseudo trigger mentions as many as possible. Evaluation on the ACE 2005 Chinese corpus justifies the effectiveness of our approach over a strong baseline. </p></blockquote></details> <details> <summary>5. <a href="https://www.aclweb.org/anthology/C12-1033/">Joint Modeling for Chinese Event Extraction with Rich Linguistic Features</a> by<i> Chen Chen, Vincent Ng</i></summary><blockquote><p align="justify"> Compared to the amount of research that has been done on English event extraction, there exists relatively little work on Chinese event extraction. We seek to push the frontiers of supervised Chinese event extraction research by proposing two extension to Li et al.'s (2012) state-of-the-art event extraction system. First, we employ a joint modeling approach to event extraction, aiming to address the error propagation problem inherent in Li et al.'s pipeline system architecture. Second, we investigate a variety of rich knowledge sources for Chinese event extraction that encode knowledge ranging from the character level to the discourse level. Experimental results on the ACE 2005 dataset show that our joint-modeling, knowledge-rich approach significantly outperforms Li et al.'s approach. </p></blockquote></details> <details> <summary>6. <a href="https://www.aclweb.org/anthology/C12-1033/">Multi-Event Extraction Guided by Global Constraints</a> by<i> Roi Reichart, Regina Barzilay</i></summary><blockquote><p align="justify"> This paper addresses the extraction of eventrecords from documents that describe multi-ple events. Specifically, we aim to identify the fields of information contained in a document and aggregate together those fields that describe the same event. To exploit the inherent connections between field extraction and event identification, we propose to model them jointly. Our model is novel in that it integrates information from separate sequential models, using global potentials that encourage the extracted event records to have desired properties. While the model contains high-order potentials, efficient approximate inference can be performed with dual decomposition. We experiment with two datasets that consist of newspaper articles describing multiple terrorism events, and show that our model substantially outperforms tra-ditional pipeline models. </p></blockquote></details> ### 2013 <details> <summary>1. <a href="https://www.aclweb.org/anthology/W13-2017/">A Hybrid approach for biomedical event extraction</a> by<i> Xuan Quang Pham, Minh Quang Le, Bao Quoc Ho </i></summary><blockquote><p align="justify"> In this paper we propose a system which uses hybrid methods that combine both rule-based and machine learning (ML)-based approaches to solve GENIA Event Extraction of BioNLP Shared Task 2013. We apply UIMA 1 Framework to support coding. There are three main stages in model: Pre-processing, trigger detection and biomedical event detection. We use dictionary and support vector machine classifier to detect event triggers. Event detection is applied on syntactic patterns which are combined with features extracted for classification. </p></blockquote></details> <details> <summary>2. <a href="https://www.aclweb.org/anthology/P13-1145/">Argument Inference from Relevant Event Mentions in Chinese Argument Extraction</a> by<i> Peifeng Li, Qiaoming Zhu, Guodong Zhou</i></summary><blockquote><p align="justify"> As a paratactic language, sentence-level argument extraction in Chinese suffers much from the frequent occurrence of ellipsis with regard to inter-sentence arguments. To resolve such problem, this paper proposes a novel global argument inference model to explore specific relationships, such as Coreference, Sequence and Parallel, among relevant event mentions to recover those intersentence arguments in the sentence, discourse and document layers which represent the cohesion of an event or a topic. Evaluation on the ACE 2005 Chinese corpus justifies the effectiveness of our global argument inference model over a state-of-the-art baseline. </p></blockquote></details> <details> <summary>3. <a href="https://www.ijcai.org/Proceedings/13/Papers/313.pdf">Joint Modeling of Argument Identification and Role Determination in Chinese Event Extraction with Discourse-Level Information</a> by<i> Peifeng Li, Qiaoming Zhu and Guodong Zhou</i></summary><blockquote><p align="justify"> Argument extraction is a challenging task in event extraction. However, most of previous studies focused on intra-sentence information and failed to extract inter-sentence arguments. This paper proposes a discourse-level joint model of argument identification and role determination to infer those inter-sentence arguments in a discourse. Moreover, to better represent the relationship among relevant event mentions and the relationship between an event mention and its arguments in a discourse, this paper introduces various kinds of corpus-based and discourse-based constraints in the joint model, either automatically learned or linguistically motivated. Evaluation on the ACE 2005 Chinese corpus justifies the effectiveness of our joint model over a strong baseline in Chinese argument extraction, in particular argument identification. </p></blockquote></details> <details> <summary>4. <a href="https://www.aclweb.org/anthology/P13-1008/">Joint Event Extraction via Structured Prediction with Global Features</a> by<i> Qi Li, Heng Ji, Liang Huang</i></summary><blockquote><p align="justify"> Traditional approaches to the task of ACE event extraction usually rely on sequential pipelines with multiple stages, which suffer from error propagation since event triggers and arguments are predicted in isolation by independent local classifiers. By contrast, we propose a joint framework based on structured prediction which extracts triggers and arguments together so that the local predictions can be mutually improved. In addition, we propose to incorporate global features which explicitly capture the dependencies of multiple triggers and arguments. Experimental results show that our joint approach with local features outperforms the pipelined baseline, and adding global features further improves the performance significantly. Our approach advances state-ofthe-art sentence-level event extraction, and even outperforms previous argument labeling methods which use external knowledge from other sentences and documents. </p></blockquote></details> <details> <summary>5. <a href="https://www.aclweb.org/anthology/W13-2006/">Biomedical Event Extraction by Multi-class Classification of Pairs of Text Entities</a> by<i> Xiao Liu, Antoine Bordes, Yves Grandvalet</i></summary><blockquote><p align="justify"> Biomedical event extraction from articles as become a popular research topic driven by important applications, such as the automatic update of dedicated knowledge base. Most existing approaches are either pipeline models of specific classifiers, usually subject to cascading errors, or joint structured models, more efficient but also more costly and more involved to train. We propose here a system based on a pairwise model that transforms event extraction into a simple multi-class problem of classifying pairs of text entities. Such pairs are recursively provided to the classifier, so as to extract events involving other events as arguments. Our model is more direct than the usual pipeline approaches, and speeds up inference compared to joint models. We report here the best results reported so far on the BioNLP 2011 and 2013 Genia tasks. </p></blockquote></details> ### 2014 <details> <summary>1. <a href="https://www.hindawi.com/journals/bmri/2014/205239/">A Novel Feature Selection Strategy for Enhanced Biomedical Event Extraction Using the Turku System</a> by<i> Jingbo Xia, Alex Chengyu Fang, and Xing Zhang</i></summary><blockquote><p align="justify"> Feature selection is of paramount importance for text-mining classifiers with high-dimensional features. The Turku Event Extraction System (TEES) is the best performing tool in the GENIA BioNLP 2009/2011 shared tasks, which relies heavily on high-dimensional features. This paper describes research which, based on an implementation of an accumulated effect evaluation (AEE) algorithm applying the greedy search strategy, analyses the contribution of every single feature class in TEES with a view to identify important features and modify the feature set accordingly. With an updated feature set, a new system is acquired with enhanced performance which achieves an increased -score of 53.27% up from 51.21% for Task 1 under strict evaluation criteria and 57.24% according to the approximate span and recursive criterion. </p></blockquote></details> <details> <summary>2. <a href="https://www.aclweb.org/anthology/D14-1090/">Relieving the Computational Bottleneck: Joint Inference for Event Extraction with High-Dimensional Features</a> by<i> Deepak Venugopal, Chen Chen, Vibhav Gogate, Vincent Ng</i></summary><blockquote><p align="justify"> Several state-of-the-art event extraction systems employ models based on Support Vector Machines (SVMs) in a pipeline architecture, which fails to exploit the joint dependencies that typically exist among events and arguments. While there have been attempts to overcome this limitation using Markov Logic Networks (MLNs), it remains challenging to perform joint inference in MLNs when the model encodes many high-dimensional sophisticated features such as those essential for event extraction. In this paper, we propose a new model for event extraction that combines the power of MLNs and SVMs, dwarfing their limitations. The key idea is to reliably learn and process high-dimensional features using SVMs; encode the outputof SVMs as low-dimensional, soft formulas in MLNs; and use the superior joint inferencing power of MLNs to enforce joint consistency constraints over the soft formulas. We evaluate our approach for the task of extracting biomedical events onthe BioNLP 2013, 2011 and 2009 Geniashared task datasets. Our approach yields the best F1 score to date on the BioNLP’13 (53.61) and BioNLP’11 (58.07) datasets and the second-best F1 score to date on theBioNLP’09 dataset (58.16). </p></blockquote></details> <details> <summary>3. <a href="http://aclweb.org/anthology/P14-5007">Real-Time Detection, Tracking, and Monitoring of Automatically Discovered Events in Social Media</a> by<i> Osborne, Miles and Moran, Sean and McCreadie, Richard and Von Lunen, Alexander and Sykora, Martin and Cano, Elizabeth and Ireson, Neil and Macdonald, Craig and Ounis, Iadh and He, Yulan and Jackson, Tom and Ciravegna, Fabio and O'Brien, Ann </i></summary><blockquote><p align="justify"> We introduce ReDites, a system for realtime event detection, tracking, monitoring and visualisation. It is designed to assist Information Analysts in understanding and exploring complex events as they unfold in the world. Events are automatically detected from the Twitter stream. Then those that are categorised as being security-relevant are tracked, geolocated, summarised and visualised for the end-user. Furthermore, the system tracks changes in emotions over events, signalling possible flashpoints or abatement. We demonstrate the capabilities of ReDites using an extended use case from the September 2013 Westgate shooting incident. Through an evaluation of system latencies, we also show that enriched events are made available for users to explore within seconds of that event occurring. </p></blockquote></details> <details> <summary>3. <a href="https://www.cs.cmu.edu/~hovy/papers/14LREC-event-coref.pdf">A Simple Bayesian Modelling Approach to Event Extraction from Twitter</a> by<i> Zhengzhong Liu, Jun Araki, Eduard Hovy, Teruko Mitamura</i></summary><blockquote><p align="justify"> Event coreference is an important task for full text analysis. However, previous work uses a variety of approaches, sources and evaluation,making the literature confusing and the results incommensurate. We provide a description of the differences to facilitate future research. Second, we present a supervised method for event coreference resolution that uses a rich feature set and propagates information alternatively between events and their arguments, adapting appropriately for each type of argument. </p></blockquote></details> <details> <summary>4. <a href="http://aclweb.org/anthology/P14-2114">Supervised Within-Document Event Coreference using Information Propagation</a> by<i> Deyu Zhou, Liangyu Chen, Yulan He</i></summary><blockquote><p align="justify"> With the proliferation of social media sites, social streams have proven to contain the most up-to-date information on current events. Therefore, it is crucial to extract events from the social streams such as tweets. However, it is not straightforward to adapt the existing event extraction systems since texts in social media are fragmented and noisy. In this paper we propose a simple and yet effective Bayesian model, called Latent Event Model (LEM), to extract structured representation of events from social media. LEM is fully unsupervised and does not require annotated data for training. We evaluate LEM on a Twitter corpus. Experimental results show that the proposed model achieves 83% in F-measure, and outperforms the state-of-the-art baseline by over 7%. </p></blockquote></details> <details> <summary>5. <a href="http://aclweb.org/anthology/P14-2136">Bilingual Event Extraction: a Case Study on Trigger Type Determination</a> by<i> Zhu, Zhu and Li, Shoushan and Zhou, Guodong and Xia, Rui </i></summary><blockquote><p align="justify"> Event extraction generally suffers from the data sparseness problem. In this paper, we address this problem by utilizing the labeled data from two different languages. As a preliminary study, we mainly focus on the subtask of trigger type determination in event extraction. To make the training data in different languages help each other, we propose a uniform text representation with bilingual features to represent the samples and handle the difficulty of locating the triggers in the translated text from both monolingual and bilingual perspectives. Empirical studies demonstrate the effectiveness of the proposed approach to bilingual classification on trigger type determination. </p></blockquote></details> ### 2015 <details> <summary>1. <a href="https://www.aclweb.org/anthology/P15-3005/">Disease event detection based on deep modality analysis</a> by<i> Yoshiaki Kitagawa, Mamoru Komachi, Eiji Aramaki, Naoaki Okazaki, Hiroshi Ishikawa</i></summary><blockquote><p align="justify"> Social media has attracted attention because of its potential for extraction of information of various types. For example, information collected from Twitter enables us to build useful applications such as predicting an epidemic of influenza. However, using text information from social media poses challenges for event detection because of the unreliable nature of user-generated texts, which often include counter-factual statements. Consequently, this study proposes the use of modality features to improve disease event detection from Twitter messages, or "tweets". Experimental results demonstrate that the combination of a modality dictionary and a modality analyzer improves the F1-score by 3.5 points. </p></blockquote></details> <details> <summary>2. <a href="https://www.aclweb.org/anthology/S15-1018/">Event Extraction as Frame-Semantic Parsing</a> by<i> Alex Judea, Michael Strube</i></summary><blockquote><p align="justify"> Based on the hypothesis that frame-semantic parsing and event extraction are structurally identical tasks, we retrain SEMAFOR, a state-of-the-art frame-semantic parsing system to predict event triggers and arguments. We describe how we change SEMAFOR to be better suited for the new task and show that it performs comparable to one of the best systems in event extraction. We also describe a bias in one of its models and propose a feature factorization which is better suited for this model. </p></blockquote></details> <details> <summary>3. <a href="https://ieeexplore.ieee.org/document/7244210">Extracting Biomedical Event with Dual Decomposition Integrating Word Embeddings</a> by<i> Lishuang Li ; Shanshan Liu ; Meiyue Qin ; Yiwen Wang ; Degen Huang</i></summary><blockquote><p align="justify"> Extracting biomedical event from literatures has attracted much attention recently. By now, most of the state-of-the-art systems have been based on pipelines which suffer from cascading errors, and the words encoded by one-hot are unable to represent the semantic information. Joint inference with dual decomposition and novel word embeddings are adopted to address the two problems, respectively, in this work. Word embeddings are learnt from large scale unlabeled texts and integrated as an unsupervised feature into other rich features based on dependency parse graphs to detect triggers and arguments. The proposed system consists of four components: trigger detector, argument detector, jointly inference with dual decomposition, and rule-based semantic post-processing, and outperforms the state-of-the-art systems. On the development set of BioNLP'09, the F-score is 59.77 percent on the primary task, which is 0.96 percent higher than the best system. On the test set of BioNLP'11, the F-score is 56.09 and 0.89 percent higher than the best published result that do not adopt additional techniques. On the test set of BioNLP'13, the F-score reaches 53.19 percent which is 2.22 percent higher than the best result. </p></blockquote></details> <details> <summary>4. <a href="https://www.aclweb.org/anthology/D15-1247/">Joint event trigger identification and event coreference resolution with structured perceptron</a> by<i> Jun Araki, Teruko Mitamura</i></summary><blockquote><p align="justify"> Events and their coreference offer useful semantic and discourse resources. We show that the semantic and discourse aspects of events interact with each other. However, traditional approaches addressed event extraction and event coreference resolution either separately or sequentially, which limits their interactions. This paper proposes a document-level structured learning model that simultaneously identifies event triggers and resolves event coreference. We demonstrate that the joint model outperforms a pipelined model by 6.9 BLANC F1 and 1.8 CoNLL F1 points in event coreference resolution using a corpus in the biology domain. </p></blockquote></details> <details> <summary>5. <a href="http://aclweb.org/anthology/P15-1056">Bring you to the past: Automatic Generation of Topically Relevant Event Chronicles</a> by<i> Ge, Tao and Pei, Wenzhe and Ji, Heng and Li, Sujian and Chang, Baobao and Sui, Zhifang </i></summary><blockquote><p align="justify"> An event chronicle provides people with an easy and fast access to learn the past. In this paper, we propose the first novel approach to automatically generate a topically relevant event chronicle during a certain period given a reference chronicle during another period. Our approach consists of two core components – a timeaware hierarchical Bayesian model for event detection, and a learning-to-rank model to select the salient events to construct the final chronicle. Experimental results demonstrate our approach is promising to tackle this new problem. </p></blockquote></details> <details> <summary>6. <a href="http://aclweb.org/anthology/P15-3005">Disease Event Detection based on Deep Modality Analysis</a> by<i> Kitagawa, Yoshiaki and Komachi, Mamoru and Aramaki, Eiji and Okazaki, Naoaki and Ishikawa, Hiroshi </i></summary><blockquote><p align="justify"> Social media has attracted attention because of its potential for extraction of information of various types. For example, information collected from Twitter enables us to build useful applications such as predicting an epidemic of influenza. However, using text information from social media poses challenges for event detection because of the unreliable nature of user-generated texts, which often include counter-factual statements. </p></blockquote></details> <details> <summary>7. <a href="http://aclweb.org/anthology/P15-2061">Seed-Based Event Trigger Labeling: How far can event descriptions get us?</a> by<i> Bronstein, Ofer and Dagan, Ido and Li, Qi and Ji, Heng and Frank, Anette </i></summary><blockquote><p align="justify"> The task of event trigger labeling is typically addressed in the standard supervised setting: triggers for each target event type are annotated as training data, based on annotation guidelines. We propose an alternative approach, which takes the example trigger terms mentioned in the guidelines as seeds, and then applies an eventindependent similarity-based classifier for trigger labeling. This way we can skip manual annotation for new event types, while requiring only minimal annotated training data for few example events at system setup. Our method is evaluated on the ACE-2005 dataset, achieving 5.7\% F1 improvement over a state-of-the-art supervised system which uses the full training data. </p></blockquote></details> ### 2016 <details> <summary>1. <a href="https://www.aclweb.org/anthology/W16-6308/">Biomolecular Event Extraction using a Stacked Generalization based Classifier</a> by<i> Amit Majumder, Asif Ekbal, Sudip Kumar Naskar</i></summary><blockquote><p align="justify"> In this paper we propose a stacked generalization (or stacking) model for event extraction in bio-medical text. Event extraction deals with the process of extracting detailed biological phenomenon, which is more challenging compared to the traditional binary relation extraction such as protein-protein interaction. The overall process consists of mainly three steps: event trigger detection, argument extraction by edge detection and finding correct combination of arguments. In stacking, we use Linear Support Vector Classification (Linear SVC), Logistic Regression (LR) and Stochastic Gradient Descent (SGD) as base-level learning algorithms. As meta-level learner we use Linear SVC. In edge detection step, we find out the arguments of triggers detected in trigger detection step using a SVM classifier. To find correct combination of arguments, we use rules generated by studying the properties of bio-molecular event expressions, and form an event expression consisting of event trigger, its class and arguments. The output of trigger detection is fed to edge detection for argument extraction. Experiments on benchmark datasets of BioNLP2011 show the recall, precision and Fscore of 48.96%, 66.46% and 56.38%, respectively. Comparisons with the existing systems show that our proposed model attains state-of-the-art performance. </p></blockquote></details> <details> <summary>2. <a href="https://www.aclweb.org/anthology/P16-1116/">RBPB: Regularization-Based Pattern Balancing Method for Event Extraction</a> by<i> Lei Sha, Jing Liu, Chin-Yew Lin, Sujian Li, Baobao Chang, Zhifang Sui </i></summary><blockquote><p align="justify"> Event extraction is a particularly challenging information extraction task, which intends to identify and classify event triggers and arguments from raw text. In recent works, when determining event types (trigger classification), most of the works are either pattern-only or feature-only. However, although patterns cannot cover all representations of an event, it is still a very important feature. In addition, when identifying and classifying arguments, previous works consider each candidate argument separately while ignoring the relationship between arguments. This paper proposes a Regularization-Based Pattern Balancing Method (RBPB). Inspired by the progress in representation learning, we use trigger embedding, sentence-level embedding and pattern features together as our features for trigger classification so that the effect of patterns and other useful features can be balanced. In addition, RBPB uses a regularization method to take advantage of the relationship between arguments. Experiments show that we achieve results better than current state-of-art equivalents. </p></blockquote></details> <details> <summary>3. <a href="https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/viewPaper/11990">A Probabilistic Soft Logic Based Approach to Exploiting Latent and Global Information in Event Classification</a> by<i> Shulin Liu, Kang Liu, Shizhu He, Jun Zhao</i></summary><blockquote><p align="justify"> Global information such as event-event association, and latent local information such as fine-grained entity types, are crucial to event classification. However, existing methods typically focus on sophisticated local features such as part-of-speech tags, either fully or partially ignoring the aforementioned information. By contrast, this paper focuses on fully employing them for event classification. We notice that it is difficult to encode some global information such as event-event association for previous methods. To resolve this problem, we propose a feasible approach which encodes global information in the form of logic using Probabilistic Soft Logic model. Experimental results show that, our proposed approach advances state-of-the-art methods, and achieves the best F1 score to date on the ACE data set. </p></blockquote></details> <details> <summary>4. <a href="https://www.aclweb.org/anthology/C16-1215/">Incremental Global Event Extraction</a> by<i> Alex Judea, Michael Strube</i></summary><blockquote><p align="justify"> Event extraction is a difficult information extraction task. Li et al. (2014) explore the benefits of modeling event extraction and two related tasks, entity mention and relation extraction, jointly. This joint system achieves state-of-the-art performance in all tasks. However, as a system operating only at the sentence level, it misses valuable information from other parts of the document. In this paper, we present an incremental easy-first approach to make the global context of the entire document available to the intra-sentential, state-of-the-art event extractor. We show that our method robustly increases performance on two datasets, namely ACE 2005 and TAC 2015. </p></blockquote></details> <details> <summary>5. <a href="https://www.aclweb.org/anthology/N16-1033/">Joint Extraction of Events and Entities within a Document Context</a> by<i> Bishan Yang, Tom M. Mitchell</i></summary><blockquote><p align="justify"> Events and entities are closely related; entities are often actors or participants in events and events without entities are uncommon. The interpretation of events and entities is highly contextually dependent. Existing work in information extraction typically models events separately from entities, and performs inference at the sentence level, ignoring the rest of the document. In this paper, we propose a novel approach that models the dependencies among variables of events, entities, and their relations, and performs joint inference of these variables across a document. The goal is to enable access to document-level contextual information and facilitate context-aware predictions. We demonstrate that our approach substantially outperforms the state-of-the-art methods for event extraction as well as a strong baseline for entity extraction. </p></blockquote></details> ### 2020 <details> <summary>1. <a href="https://www.tandfonline.com/doi/full/10.1080/24751839.2020.1763007">Event detection based on open information extraction and ontology</a> by<i> Sihem Sahnoun, Samir Elloumi, Sadok Ben Yahia</i></summary><blockquote><p align="justify"> Most of the information is available in the form of unstructured textual documents due to the growth of information sources (the Web for example). In this respect, to extract a set of events from texts written in natural language in the management change event, we have been introduced an open information extraction (OIE) system. For instance, in the management change event, a PERSON might be either the new coming person to the company or the leaving one. As a result, the Adaptive CRF approach (A-CRF) has shown good performance results. However, it requires a lot of expert intervention during the construction of classifiers, which is time consuming. To palpate such a downside, we introduce an approach that reduces the expert intervention during the relation extraction. Also, the named entity recognition and the reasoning, which are automatic and based on techniques of adaptation and correspondence, were implemented. Carried out experiments show the encouraging results of the main approaches of the literature. </p></blockquote></details> <details> <summary>2. <a href="https://www.aclweb.org/anthology/2020.lrec-1.258/">Event Extraction from Unstructured Amharic Text</a> by<i> ephrem tadesse, Rosa Tsegaye, Kuulaa Qaqqabaa</i></summary><blockquote><p align="justify"> In information extraction, event extraction is one of the types that extract the specific knowledge of certain incidents from texts. Event extraction has been done on different languages text but not on one of the Semitic language, Amharic. In this study, we present a system that extracts an event from unstructured Amharic text. The system has designed by the integration of supervised machine learning and rule-based approaches. We call this system a hybrid system. The system uses the supervised machine learning to detect events from the text and the handcrafted and the rule-based rules to extract the event from the text. For the event extraction, we have been using event arguments. Event arguments identify event triggering words or phrases that clearly express the occurrence of the event. The event argument attributes can be verbs, nouns, sometimes adjectives (such as ̃rg/wedding) and time as well. The hybrid system has compared with the standalone rule-based method that is well known for event extraction. The study has shown that the hybrid system has outperformed the standalone rule-based method. </p></blockquote></details> ## Deep learning [:arrow_up:](#table-of-contents) ### 2015 <details> <summary>1. <a href="https://www.aclweb.org/anthology/P15-2060/">Event detection and domain adaptation with convolutional neural networks</a> by<i> Thien Huu Nguyen, Ralph Grishman </i>(<a href="https://github.com/ThanhChinhBK/event_detector">Github</a>)</summary><blockquote><p align="justify"> We study the event detection problem using convolutional neural networks (CNNs) that overcome the two fundamental limitations of the traditional feature-based approaches to this task: complicated feature engineering for rich feature sets and error propagation from the preceding stages which generate these features. The experimental results show that the CNNs outperform the best reported feature-based systems in the general setting as well as the domain adaptation setting without resorting to extensive external resources. </p></blockquote></details> <details> <summary>2. <a href="https://www.aclweb.org/anthology/P15-1017/">Event Extraction via Dynamic Multi-Pooling Convolutional Neural Networks</a> by<i> Yubo Chen, Liheng Xu, Kang Liu, Daojian Zeng, Jun Zhao</i> (<a href="https://github.com/zhangluoyang/cnn-for-auto-event-extract">Github</a>)</summary><blockquote><p align="justify"> Traditional approaches to the task of ACE event extraction primarily rely on elaborately designed features and complicated natural language processing (NLP) tools. These traditional approaches lack generalization, take a large amount of human effort and are prone to error propagation and data sparsity problems. This paper proposes a novel event-extraction method, which aims to automatically extract lexical-level and sentence-level features without using complicated NLP tools. We introduce a word-representation model to capture meaningful semantic regularities for words and adopt a framework based on a convolutional neural network (CNN) to capture sentence-level clues. However, CNN can only capture the most important information in a sentence and may miss valuable facts when considering multiple-event sentences. We propose a dynamic multi-pooling convolutional neural network (DMCNN), which uses a dynamic multi-pooling layer according to event triggers and arguments, to reserve more crucial information. The experimental results show that our approach significantly outperforms other state-of-the-art methods. </p></blockquote></details> <details> <summary>3. <a href="https://www.semanticscholar.org/paper/Event-Nugget-Detection%2C-Classification-and-using-Reimers-Gurevych/1b5cf83ea210e1793526c915e132d21e53f6726f">Event Nugget Detection, Classification and Coreference Resolution using Deep Neural Networks and eXtreme Grandient Boosting</a> by<i> Nils Reimers, Iryna Gurevych </i> (<a href="https://github.com/UKPLab/tac2015-event-detection">Github</a>)</summary><blockquote><p align="justify"> Traditional approaches to the task of ACE event extraction primarily rely on elaborately designed features and complicated natural language processing (NLP) tools. These traditional approaches lack generalization, take a large amount of human effort and are prone to error propagation and data sparsity problems. This paper proposes a novel event-extraction method, which aims to automatically extract lexical-level and sentence-level features without using complicated NLP tools. We introduce a word-representation model to capture meaningful semantic regularities for words and adopt a framework based on a convolutional neural network (CNN) to capture sentence-level clues. However, CNN can only capture the most important information in a sentence and may miss valuable facts when considering multiple-event sentences. We propose a dynamic multi-pooling convolutional neural network (DMCNN), which uses a dynamic multi-pooling layer according to event triggers and arguments, to reserve more crucial information. The experimental results show that our approach significantly outperforms other state-of-the-art methods. </p></blockquote></details> ### 2016 <details> <summary>1. <a href="https://www.aclweb.org/anthology/D16-1085/">Modeling Skip-Grams for Event Detection with Convolutional Neural Networks</a> by<i> Thien Huu Nguyen, Ralph Grishman</i></summary><blockquote><p align="justify"> Convolutional neural networks (CNN) have achieved the top performance for event detection due to their capacity to induce the underlying structures of the k-grams in the sentences. However, the current CNN-based event detectors only model the consecutive k-grams and ignore the non-consecutive kgrams that might involve important structures for event detection. In this work, we propose to improve the current CNN models for ED by introducing the non-consecutive convolution. Our systematic evaluation on both the general setting and the domain adaptation setting demonstrates the effectiveness of the non-consecutive CNN model, leading to the significant performance improvement over the current state-of-the-art systems. </p></blockquote></details> <details> <summary>2. <a href="https://link.springer.com/chapter/10.1007%2F978-3-319-50496-4_27">Joint Event Extraction Based on Skip-Window Convolutional Neural Networks</a> by<i> Zhengkuan Zhang, Weiran Xu, Qianqian Chen</i></summary><blockquote><p align="justify"> Traditional approaches to the task of ACE event extraction are either the joint model with elaborately designed features which may lead to generalization and data-sparsity problems, or the word-embedding model based on a two-stage, multi-class classification architecture, which suffers from error propagation since event triggers and arguments are predicted in isolation. This paper proposes a novel event-extraction method that not only extracts triggers and arguments simultaneously, but also adopts a framework based on convolutional neural networks (CNNs) to extract features automatically. However, CNNs can only capture sentence-level features, so we propose the skip-window convolution neural networks (S-CNNs) to extract global structured features, which effectively capture the global dependencies of every token in the sentence. The experimental results show that our approach outperforms other state-of-the-art methods. </p></blockquote></details> <details> <summary>3. <a href="https://www.aclweb.org/anthology/N16-1034/">Joint Event Extraction via Recurrent Neural Networks</a> by<i> Thien Huu Nguyen, Kyunghyun Cho, Ralph Grishman </i>(<a href="https://github.com/anoperson/jointEE-NN">Github</a>)</summary><blockquote><p align="justify"> Event extraction is a particularly challenging problem in information extraction. The stateof-the-art models for this problem have either applied convolutional neural networks in a pipelined framework (Chen et al., 2015) or followed the joint architecture via structured prediction with rich local and global features (Li et al., 2013). The former is able to learn hidden feature representations automatically from data based on the continuous and generalized representations of words. The latter, on the other hand, is capable of mitigating the error propagation problem of the pipelined approach and exploiting the inter-dependencies between event triggers and argument roles via discrete structures. In this work, we propose to do event extraction in a joint framework with bidirectional recurrent neural networks, thereby benefiting from the advantages of the two models as well as addressing issues inherent in the existing approaches. We systematically investigate different memory features for the joint model and demonstrate that the proposed model achieves the state-of-the-art performance on the ACE 2005 dataset. </p></blockquote></details> <details> <summary>4. <a href="https://www.aclweb.org/anthology/P16-2060/">Event Nugget Detection with Forward-Backward Recurrent Neural Networks</a> by<i> Reza Ghaeini, Xiaoli Fern, Liang Huang, Prasad Tadepalli</i></summary><blockquote><p align="justify"> Traditional event detection methods heavily rely on manually engineered rich features. Recent deep learning approaches alleviate this problem by automatic feature engineering. But such efforts, like tradition methods, have so far only focused on single-token event mentions, whereas in practice events can also be a phrase. We instead use forward-backward recurrent neural networks (FBRNNs) to detect events that can be either words or phrases. To the best our knowledge, this is one of the first efforts to handle multi-word events and also the first attempt to use RNNs for event detection. Experimental results demonstrate that FBRNN is competitive with the state-of-the-art methods on the ACE 2005 and the Rich ERE 2015 event detection tasks. </p></blockquote></details> <details> <summary>5. <a href="https://link.springer.com/chapter/10.1007/978-3-319-47674-2_17">Event Extraction via Bidirectional Long Short-Term Memory Tensor Neural Network</a> by<i> Yubo Chen, Shulin Liu, Shizhu He, Kang LiuJun Zhao</i></summary><blockquote><p align="justify"> Traditional approaches to the task of ACE event extraction usually rely on complicated natural language processing (NLP) tools and elaborately designed features. Which suffer from error propagation of the existing tools and take a large amount of human effort. And nearly all of approaches extract each argument of an event separately without considering the interaction between candidate arguments. By contrast, we propose a novel event-extraction method, which aims to automatically extract valuable clues without using complicated NLP tools and predict all arguments of an event simultaneously. In our model, we exploit a context-aware word representation model based on Long Short-Term Memory Networks (LSTM) to capture the semantics of words from plain texts. In addition, we propose a tensor layer to explore the interaction between candidate arguments and predict all arguments simultaneously. The experimental results show that our approach significantly outperforms other state-of-the-art methods. </p></blockquote></details> <details> <summary>6. <a href="https://tac.nist.gov/publications/2016/participant.papers/TAC2016.wip.proceedings.pdf">WIP Event Detection System at TAC KBP 2016 Event Nugget Track</a> by<i> Ying Zeng, Bingfeng Luo, Yansong Feng, Dongyan Zhao</i></summary><blockquote><p align="justify"> Event detection aims to extract events with specific types from unstructured data, which is the crucial and challenging task in event related applications, such as event coreference resolution and event argument extraction. In this paper, we propose an event detection system that combines traditional feature-based methods and novel neural network (NN) models. Experiments show that our ensemble approaches can achieve promising performance in the Event Nugget Detection task. </p></blockquote></details> <details> <summary>7. <a href="https://link.springer.com/chapter/10.1007%2F978-3-319-50496-4_23">A Convolution BiLSTM Neural Network Model for Chinese Event Extraction</a> by<i> Ying Zeng, Honghui Yang, Yansong Feng, Zheng Wang, Dongyan Zhao</i></summary><blockquote><p align="justify"> Chinese event extraction is a challenging task in information extraction. Previous approaches highly depend on sophisticated feature engineering and complicated natural language processing (NLP) tools. In this paper, we first come up with the language specific issue in Chinese event extraction, and then propose a convolution bidirectional LSTM neural network that combines LSTM and CNN to capture both sentence-level and lexical information without any hand-craft features. Experiments on ACE 2005 dataset show that our approaches can achieve competitive performances in both trigger labeling and argument role labeling. </p></blockquote></details> <details> <summary>8. <a href="https://www.aclweb.org/anthology/P16-2011/">A Language-Independent Neural Network for Event Detection</a> by<i> Xiaocheng Feng, Lifu Huang, Duyu Tang, Heng Ji, Bing Qin, Ting Liu</i></summary><blockquote><p align="justify"> Event detection remains a challenge because of the difficulty of encoding the word semantics in various contexts. Previous approaches have heavily depended on language-specific knowledge and preexisting natural language processing tools. However, not all languages have such resources and tools available compared with English language. A more promising approach is to automatically learn effective features from data, without relying on language-specific resources. In this study, we develop a language-independent neural network to capture both sequence and chunk information from specific contexts and use them to train an event detector for multiple languages without any manually encoded features. Experiments show that our approach can achieve robust, efficient and accurate results for various languages. In the ACE 2005 English event detection task, our approach achieved a 73.4% F-score with an average of 3.0% absolute improvement compared with state-of-the-art. Additionally, our experimental results are competitive for Chinese and Spanish. </p></blockquote></details> ### 2017 <details> <summary>1. <a href="https://link.springer.com/chapter/10.1007%2F978-3-319-69005-6_11">Improving Event Detection via Information Sharing among Related Event Types</a> by<i> Shulin Liu, Yubo Chen, Kang Liu, Jun Zhao, Zhunchen Luo, Wei Luo</i></summary><blockquote><p align="justify"> Event detection suffers from data sparseness and label imbalance problem due to the expensive cost of manual annotations of events. To address this problem, we propose a novel approach that allows for information sharing among related event types. Specifically, we employ a fully connected three-layer artificial neural network as our basic model and propose a type-group regularization term to achieve the goal of information sharing. We conduct experiments with different configurations of type groups, and the experimental results show that information sharing among related event types remarkably improves the detecting performance. Compared with state-of-the-art methods, our proposed approach achieves a better F-1 score on the widely used ACE 2005 event evaluation dataset. </p></blockquote></details> <details> <summary>2. <a href="http://oro.open.ac.uk/49639/">On semantics and deep learning for event detection in crisis situations</a> by<i> Burel Grégoire; Saif Hassan; Fernandez Miriam and Alani Harith</i></summary><blockquote><p align="justify"> In this paper, we introduce Dual-CNN, a semantically-enhanced deep learning model to target the problem of event detection in crisis situations from social media data. A layer of semantics is added to a traditional Convolutional Neural Network (CNN) model to capture the contextual information that is generally scarce in short, ill-formed social media messages. Our results show that our methods are able to successfully identify the existence of events, and event types (hurricane, floods, etc.) accurately (> 79% F-measure), but the performance of the model significantly drops (61% F-measure) when identifying fine-grained event-related information (affected individuals, damaged infrastructures, etc.). These results are competitive with more traditional Machine Learning models, such as SVM. </p></blockquote></details> <details> <summary>3. <a href="https://www.aclweb.org/anthology/I17-1036/">Exploiting Document Level Information to Improve Event Detection via Recurrent Neural Networks</a> by<i> Shaoyang Duan, Ruifang He, Wenli Zhao</i></summary><blockquote><p align="justify"> This paper tackles the task of event detection, which involves identifying and categorizing events. The previous work mainly exist two problems: (1) the traditional feature-based methods apply cross-sentence information, yet need taking a large amount of human effort to design complicated feature sets and inference rules; (2) the representation-based methods though overcome the problem of manually extracting features, while just depend on local sentence representation. Considering local sentence context is insufficient to resolve ambiguities in identifying particular event types, therefore, we propose a novel document level Recurrent Neural Networks (DLRNN) model, which can automatically extract cross-sentence clues to improve sentence level event detection without designing complex reasoning rules. Experiment results show that our approach outperforms other state-of-the-art methods on ACE 2005 dataset without external knowledge base. </p></blockquote></details> <details> <summary>4. <a href="https://www.aclweb.org/anthology/W17-2315/">Biomedical Event Extraction using Abstract Meaning Representation</a> by<i> Sudha Rao, Daniel Marcu, Kevin Knight, Hal Daumé III</i></summary><blockquote><p align="justify"> We propose a novel, Abstract Meaning Representation (AMR) based approach to identifying molecular events/interactions in biomedical text. Our key contributions are: (1) an empirical validation of our hypothesis that an event is a subgraph of the AMR graph, (2) a neural network-based model that identifies such an event subgraph given an AMR, and (3) a distant supervision based approach to gather additional training data. We evaluate our approach on the 2013 Genia Event Extraction dataset and show promising results. </p></blockquote></details> <details> <summary>5. <a href="https://www.aclweb.org/anthology/P17-1164/">Exploiting Argument Information to Improve Event Detection via Supervised Attention Mechanisms</a> by<i> Shulin Liu, Yubo Chen, Kang Liu, Jun Zhao</i></summary><blockquote><p align="justify"> This paper tackles the task of event detection (ED), which involves identifying and categorizing events. We argue that arguments provide significant clues to this task, but they are either completely ignored or exploited in an indirect manner in existing detection approaches. In this work, we propose to exploit argument information explicitly for ED via supervised attention mechanisms. In specific, we systematically investigate the proposed model under the supervision of different attention strategies. Experimental results show that our approach advances state-of-the-arts and achieves the best F1 score on ACE 2005 dataset. </p></blockquote></details> <details> <summary>6. <a href="https://www.aclweb.org/anthology/D17-1163/">Identifying civilians killed by police with distantly supervised entity-event extraction</a> by<i> Katherine Keith, Abram Handler, Michael Pinkham, Cara Magliozzi, Joshua McDuffie, Brendan O’Connor</i> (<a href="https://github.com/slanglab/policefatalities_emnlp2017">Github</a>)</summary><blockquote><p align="justify"> We propose a new, socially-impactful task for natural language processing: from a news corpus, extract names of persons who have been killed by police. We present a newly collected police fatality corpus, which we release publicly, and present a model to solve this problem that uses EM-based distant supervision with logistic regression and convolutional neural network classifiers. Our model outperforms two off-the-shelf event extractor systems, and it can suggest candidate victim names in some cases faster than one of the major manually-collected police fatality databases. </p></blockquote></details> <details> <summary>7. <a href="http://aclweb.org/anthology/P17-2046">English Event Detection With Translated Language Features</a> by<i> Wei, Sam and Korostil, Igor and Nothman, Joel and Hachey, Ben </i></summary><blockquote><p align="justify"> We propose novel radical features from automatic translation for event extraction. Event detection is a complex language processing task for which it is expensive to collect training data, making generalisation challenging. We derive meaningful subword features from automatic translations into target language. Results suggest this method is particularly useful when using languages with writing systems that facilitate easy decomposition into subword features, e.g., logograms and Cangjie. The best result combines logogram features from Chinese and Japanese with syllable features from Korean, providing an additional 3.0 points f-score when added to state-of-the-art generalisation features on the TAC KBP 2015 Event Nugget task. </p></blockquote></details> <details> <summary>8. <a href="http://dl.acm.org/citation.cfm?doid=3123266.3123294">Improving Event Extraction via Multimodal Integration</a> by<i> Zhang, Tongtao and Whitehead, Spencer and Zhang, Hanwang and Li, Hongzhi and Ellis, Joseph and Huang, Lifu and Liu, Wei and Ji, Heng and Chang, Shih-Fu </i></summary><blockquote><p align="justify"> In this paper, we focus on improving Event Extraction (EE) by incorporating visual knowledge with words and phrases from text documents. We rst discover visual pa erns from large-scale textimage pairs in a weakly-supervised manner and then propose a multimodal event extraction algorithm where the event extractor is jointly trained with textual features and visual pa erns. Extensive experimental results on benchmark data sets demonstrate that the (a) proposed multimodal EE method can achieve signi cantly be er performance on event extraction: absolute 7.1\% F-score gain on event trigger labeling and 8.5\% F-score gain on event argument labeling. </p></blockquote></details> ### 2018 <details> <summary>1. <a href="https://www.aclweb.org/anthology/D18-1517/">Similar but not the Same: Word Sense Disambiguation Improves Event Detection via Neural Representation Matching</a> by<i> Weiyi Lu, Thien Huu Nguyen</i></summary><blockquote><p align="justify"> Event detection (ED) and word sense disambiguation (WSD) are two similar tasks in that they both involve identifying the classes (i.e. event types or word senses) of some word in a given sentence. It is thus possible to extract the knowledge hidden in the data for WSD, and utilize it to improve the performance on ED. In this work, we propose a method to transfer the knowledge learned on WSD to ED by matching the neural representations learned for the two tasks. Our experiments on two widely used datasets for ED demonstrate the effectiveness of the proposed method. </p></blockquote></details> <details> <summary>2. <a href="https://www.aclweb.org/anthology/D18-1127/">Exploiting Contextual Information via Dynamic Memory Network for Event Detection</a> by<i> Shaobo Liu, Rui Cheng, Xiaoming Yu, Xueqi Cheng </i>(<a href="https://github.com/AveryLiu/TD-DMN">Github</a>)</summary><blockquote><p align="justify"> The task of event detection involves identifying and categorizing event triggers. Contextual information has been shown effective on the task. However, existing methods which utilize contextual information only process the context once. We argue that the context can be better exploited by processing the context multiple times, allowing the model to perform complex reasoning and to generate better context representation, thus improving the overall performance. Meanwhile, dynamic memory network (DMN) has demonstrated promising capability in capturing contextual information and has been applied successfully to various tasks. In light of the multi-hop mechanism of the DMN to model the context, we propose the trigger detection dynamic memory network (TD-DMN) to tackle the event detection problem. We performed a five-fold cross-validation on the ACE-2005 dataset and experimental results show that the multi-hop mechanism does improve the performance and the proposed model achieves best F1 score compared to the state-of-the-art methods. </p></blockquote></details> <details> <summary>3. <a href="https://www.aclweb.org/anthology/P18-1145/">Nugget Proposal Networks for Chinese Event Detection</a> by<i> Hongyu Lin, Yaojie Lu, Xianpei Han, Le Sun</i> (<a href="https://github.com/sanmusunrise/NPNs">Github</a>)</summary><blockquote><p align="justify"> Neural network based models commonly regard event detection as a word-wise classification task, which suffer from the mismatch problem between words and event triggers, especially in languages without natural word delimiters such as Chinese. In this paper, we propose Nugget Proposal Networks (NPNs), which can solve the word-trigger mismatch problem by directly proposing entire trigger nuggets centered at each character regardless of word boundaries. Specifically, NPNs perform event detection in a character-wise paradigm, where a hybrid representation for each character is first learned to capture both structural and semantic information from both characters and words. Then based on learned representations, trigger nuggets are proposed and categorized by exploiting character compositional structures of Chinese event triggers. Experiments on both ACE2005 and TAC KBP 2017 datasets show that NPNs significantly outperform the state-of-the-art methods. </p></blockquote></details> <details> <summary>4. <a href="https://ieeexplore.ieee.org/document/8453008">Extracting Biomedical Events with Parallel Multi-Pooling Convolutional Neural Networks</a> by<i> Lishuang Li ; Yang Liu ; Meiyue Qin</i></summary><blockquote><p align="justify"> Biomedical event extraction is important for medical research and disease prevention, which has attracted much attention in recent years. Traditionally, most of the state-of-the-art systems have been based on shallow machine learning methods, which require many complex, hand-designed features. In addition, the words encoded by one-hot are unable to represent semantic information. Therefore, we utilize dependency-based embeddings to represent words semantically and syntactically. Then, we propose a parallel multi-pooling convolutional neural network (PMCNN) model to capture the compositional semantic features of sentences. Furthermore, we employ a rectified linear unit, which creates sparse representations with true zeros, and which is adapted to the biomedical event extraction, as a nonlinear function in PMCNN architecture. The experimental results from MLEE dataset show that our approach achieves an F1 score of 80.27% in trigger identification and an F1 score of 59.65% in biomedical event extraction, which performs better than other state-of-the-art methods. </p></blockquote></details> <details> <summary>5. <a href="https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16222">Jointly Extracting Event Triggers and Arguments by Dependency-Bridge RNN and Tensor-Based Argument Interaction</a> by<i> Lei Sha, Feng Qian, Baobao Chang, Zhifang Sui</i></summary><blockquote><p align="justify"> Event extraction plays an important role in natural language processing (NLP) applications including question answering and information retrieval. Traditional event extraction relies heavily on lexical and syntactic features, which require intensive human engineering and may not generalize to different datasets. Deep neural networks, on the other hand, are able to automatically learn underlying features, but existing networks do not make full use of syntactic relations. In this paper, we propose a novel dependency bridge recurrent neural network (dbRNN) for event extraction. We build our model upon a recurrent neural network, but enhance it with dependency bridges, which carry syntactically related information when modeling each word. We illustrates that simultaneously applying tree structure and sequence structure in RNN brings much better performance than only uses sequential RNN. In addition, we use a tensor layer to simultaneously capture the various types of latent interaction between candidate arguments as well as identify/classify all arguments of an event. Experiments show that our approach achieves competitive results compared with previous work. </p></blockquote></details> <details> <summary>6. <a href="https://link.springer.com/chapter/10.1007/978-3-030-01012-6_20">Learning Target-Dependent Sentence Representations for Chinese Event Detection</a> by<i> Wenbo Zhang, Xiao Ding, Ting Liu </i></summary><blockquote><p align="justify"> Chinese event detection is a particularly challenging task in information extraction. Previous work mainly consider the sequential representation of sentences. However, long-range dependencies between words in the sentences may hurt the performance of these approaches. We believe that syntactic representations can provide an effective mechanism to directly link words to their informative context in the sentences. In this paper, we propose a novel event detection model based on dependency trees. In particular, we propose transforming dependency trees to target-dependent trees where leaf nodes are words and internal nodes are dependency relations, to distinguish the target words. Experimental results on the ACE 2005 corpus show that our approach significantly outperforms state-of-the-art baseline methods. </p></blockquote></details> <details> <summary>7. <a href="https://arxiv.org/abs/1812.00195">One for All: Neural Joint Modeling of Entities and Events</a> by<i> Trung Minh Nguyen, Thien Huu Nguyen</i></summary><blockquote><p align="justify"> The previous work for event extraction has mainly focused on the predictions for event triggers and argument roles, treating entity mentions as being provided by human annotators. This is unrealistic as entity mentions are usually predicted by some existing toolkits whose errors might be propagated to the event trigger and argument role recognition. Few of the recent work has addressed this problem by jointly predicting entity mentions, event triggers and arguments. However, such work is limited to using discrete engineering features to represent contextual information for the individual tasks and their interactions. In this work, we propose a novel model to jointly perform predictions for entity mentions, event triggers and arguments based on the shared hidden representations from deep learning. The experiments demonstrate the benefits of the proposed method, leading to the state-of-the-art performance for event extraction. </p></blockquote></details> <details> <summary>8. <a href="https://www.sciencedirect.com/science/article/abs/pii/S0950705119300097?via%3Dihub">Empower event detection with bi-directional neural language model</a> by<i> Yunyan Zhang , Guangluan Xu , Yang Wang, Xiao Liang, Lei Wang, Tinglei Huang</i></summary><blockquote><p align="justify"> Event detection is an essential and challenging task in Information Extraction (IE). Recent advances in neural networks make it possible to build reliable models without complicated feature engineering. However, data scarcity hinders their further performance. Moreover, training data has been underused since majority of labels in datasets are not event triggers and contribute very little to the training process. In this paper, we propose a novel multi-task learning framework to extract more general patterns from raw data and make better use of the training data. Specifically, we present two paradigms to incorporate neural language model into event detection model on both word and character levels: (1) we use the features extracted by language model as an additional input to event detection model. (2) We use a hard parameter sharing approach between language model and event detection model. The extensive experiments demonstrate the benefits of the proposed multi-task learning framework for event detection. Compared to the previous methods, our method does not rely on any additional supervision but still beats the majority of them and achieves a competitive performance on the ACE 2005 benchmark. </p></blockquote></details> <details> <summary>9. <a href="https://arxiv.org/abs/1809.09078">Jointly Multiple Events Extraction via Attention-based Graph Information Aggregation</a> by<i> Xiao Liu, Zhunchen Luo, Heyan Huang</i> (<a href="https://github.com/lx865712528/EMNLP2018-JMEE">Github</a>)</summary><blockquote><p align="justify"> Event extraction is of practical utility in natural language processing. In the real world, it is a common phenomenon that multiple events existing in the same sentence, where extracting them are more difficult than extracting a single event. Previous works on modeling the associations between events by sequential modeling methods suffer a lot from the low efficiency in capturing very long-range dependencies. In this paper, we propose a novel Jointly Multiple Events Extraction (JMEE) framework to jointly extract multiple event triggers and arguments by introducing syntactic shortcut arcs to enhance information flow and attention-based graph convolution networks to model graph information. The experiment results demonstrate that our proposed framework achieves competitive results compared with state-of-the-art methods. </p></blockquote></details> <details> <summary>10. <a href="https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16329">Graph Convolutional Networks With Argument-Aware Pooling for Event Detection</a> by<i> Thien Huu Nguyen, Ralph Grishman</i></summary><blockquote><p align="justify"> The current neural network models for event detection have only considered the sequential representation of sentences. Syntactic representations have not been explored in this area although they provide an effective mechanism to directly link words to their informative context for event detection in the sentences. In this work, we investigate a convolutional neural network based on dependency trees to perform event detection. We propose a novel pooling method that relies on entity mentions to aggregate the convolution vectors. The extensive experiments demonstrate the benefits of the dependency based convolutional neural networks and the entity mentionbased pooling method for event detection. We achieve the state-of-the-art performance on widely used datasets with both perfect and predicted entity mentions. </p></blockquote></details> <details> <summary>11. <a href="https://link.springer.com/chapter/10.1007%2F978-3-030-04221-9_23">Chinese Event Recognition via Ensemble Model</a> by<i> Wei Liu, Zhenyu Yang, Zongtian Liu</i></summary><blockquote><p align="justify"> Event recognition is one of the most fundamental and critical field in information extraction. In this paper, Event recognition task can be divided into two sub-problems containing candidate event triggers identification and the classification of candidate event trigger words. Firstly, we use trigger vocabulary generated by trigger expansion to identify candidate event trigger, and then input sequences are generated according to the following three features: word embedding, POS (part of speech) and DP (dependency parsing). Finally multiclass classifier based on joint neural networks is introduced in the step of candidate trigger classification. The experiments in CEC (Chinese Emergency Corpus) have shown the superiority of our proposal model with a maximum F-measure of 80.55%. </p></blockquote></details> <details> <summary>12. <a href="http://ceur-ws.org/Vol-2266/T5-2.pdf">A neural network based Event extraction system for Indian languages</a> by<i> Alapan Kuila, Sarath chandra Bussa, Sudeshna Sarkar</i></summary><blockquote><p align="justify"> In this paper we have described a neural network based approach for Event extraction(EE) task which aims to discover different types of events along with the event arguments form the text documents written in Indian languages like Hindi, Tamil and English as part of our participation in the task on Event Extraction from Newswires and Social Media Text in Indian Languages at Forum for Information Retrieval Evaluation (FIRE) in 2018. A neural netork model which is a combination of Convolution neural network(CNN) and Recurrent neural network(RNN) is employed for the Event identification task. In addition to event detection, the system also extracts the event arguments which contain the information related to the events(i.e. when[Time], where[Place], Reason, Casualty, After-effect etc.). Our proposed Event Extraction model achieves f-score of 39.71, 37.42 and 39.91 on Hindi, Tamil and English dataset respectively which shows the overall performance of Event identification and argument extraction task in these three language domain. </p></blockquote></details> <details> <summary>13. <a href="https://iopscience.iop.org/article/10.1088/1742-6596/978/1/012078">5W1H Information Extraction with CNN-Bidirectional LSTM</a> by<i> A Nurdin1, N U Maulidevi</i></summary><blockquote><p align="justify"> In this work, information about who, did what, when, where, why, and how on Indonesian news articles were extracted by combining Convolutional Neural Network and Bidirectional Long Short-Term Memory. Convolutional Neural Network can learn semantically meaningful representations of sentences. Bidirectional LSTM can analyze the relations among words in the sequence. We also use word embedding word2vec for word representation. By combining these algorithms, we obtained F-measure 0.808. Our experiments show that CNN-BLSTM outperforms other shallow methods, namely IBk, C4.5, and Naïve Bayes with the F-measure 0.655, 0.645, and 0.595, respectively. </p></blockquote></details> <details> <summary>14. <a href="https://www.aclweb.org/anthology/P18-1048/">Self-regulation: Employing a Generative Adversarial Network to Improve Event Detection</a> by<i> Yu Hong, Wenxuan Zhou, Jingli Zhang, Guodong Zhou, Qiaoming Zhu</i> (<a href="https://github.com/JoeZhouWenxuan/Self-regulation-Employing-a-Generative-Adversarial-Network-to-Improve-Event-Detection">Github</a>)</summary><blockquote><p align="justify"> Due to the ability of encoding and mapping semantic information into a high-dimensional latent feature space, neural networks have been successfully used for detecting events to a certain extent. However, such a feature space can be easily contaminated by spurious features inherent in event detection. In this paper, we propose a self-regulated learning approach by utilizing a generative adversarial network to generate spurious features. On the basis, we employ a recurrent network to eliminate the fakes. Detailed experiments on the ACE 2005 and TAC-KBP 2015 corpora show that our proposed method is highly effective and adaptable. </p></blockquote></details> <details> <summary>15. <a href="http://tcci.ccf.org.cn/conference/2018/papers/51.pdf">Event Detection via Recurrent Neural Networkand Argument Prediction</a> by<i> Wentao Wu, Xiaoxu Zhu, Jiaming Tao, and Peifeng Li</i></summary><blockquote><p align="justify"> This paper tackles the task of event detection, which involves identifying and categorizing the events. Currently event detection remains a challenging task due to the difficulty at encoding the event semantics in complicate contexts. The core semantics of an event may derive from its trigger and arguments. However, most of previous studies failed to capture the argument semantics in event detection. To address this issue, this paper first provides a rule-based method to predict candidate arguments on the event types of possibilities, and then proposes a recurrent neural network model RNN-ARG with the attention mechanism for event detection to capture meaningful semantic regularities form these predicted candidate arguments. The experimental results on the ACE 2005 English corpus show that our approach achieves competitive results compared with previous work. </p></blockquote></details> <details> <summary>16. <a href="https://arxiv.org/abs/1808.08504">Event Detection with Neural Networks: A Rigorous Empirical Evaluation</a> by<i> J. Walker Orr, Prasad Tadepalli, Xiaoli Fern</i></summary><blockquote><p align="justify"> Detecting events and classifying them into predefined types is an important step in knowledge extraction from natural language texts. While the neural network models have generally led the state-of-the-art, the differences in performance between different architectures have not been rigorously studied. In this paper we present a novel GRU-based model that combines syntactic information along with temporal structure through an attention mechanism. We show that it is competitive with other neural network architectures through empirical evaluations under different random initializations and training-validation-test splits of ACE2005 dataset. </p></blockquote></details> <details> <summary>17. <a href="https://link.springer.com/chapter/10.1007/978-3-319-99495-6_15">Using Entity Relation to Improve Event Detection via Attention Mechanism</a> by<i> Jingli Zhang, Wenxuan Zhou, Yu Hong, Jianmin Yao, Min Zhang</i></summary><blockquote><p align="justify"> Identifying event instance in texts plays a critical role in the field of Information Extraction (IE). The currently proposed methods that employ neural networks have successfully solve the problem to some extent, by encoding a series of linguistic features, such as lexicon, part-of-speech and entity. However, so far, the entity relation hasn’t yet been taken into consideration. In this paper, we propose a novel event extraction method to exploit relation information for event detection (ED), due to the potential relevance between entity relation and event type. Methodologically, we combine relation and those widely used features in an attention-based network with Bidirectional Long Short-term Memory (Bi-LSTM) units. In particular, we systematically investigate the effect of relation representation between entities. In addition, we also use different attention strategies in the model. Experimental results show that our approach outperforms other state-of-the-art methods </p></blockquote></details> <details> <summary>18. <a href="https://link.springer.com/chapter/10.1007/978-3-030-05090-0_17">Event Extraction with Deep Contextualized Word Representation and Multi-attention Layer</a> by<i> Ruixue Ding, Zhoujun Li</i></summary><blockquote><p align="justify"> One common application of text mining is event extraction. The purpose of an event extraction task is to identify event triggers of a certain event type in the text and to find related arguments. In recent years, the technology to automatically extract events from text has drawn researchers’ attention. However, the existing works including feature based systems and neural network base models don’t capture the contextual information well. Besides, it is still difficult to extract deep semantic relations when finding related arguments for events. To address these issues, we propose a novel model for event extraction using multi-attention layers and deep contextualized word representation. Furthermore, we put forward an attention function suitable for event extraction tasks. Experimental results show that our model outperforms the state-of-the-art models on ACE2005. </p></blockquote></details> <details> <summary>19. <a href="https://www.mdpi.com/1999-5903/10/10/95">Chinese Event Extraction Based on Attention and Semantic Features: A Bidirectional Circular Neural Network</a> by<i> Yue Wu; Junyi Zhang</i></summary><blockquote><p align="justify"> Chinese event extraction uses word embedding to capture similarity, but suffers when handling previously unseen or rare words. From the test, we know that characters may provide some information that we cannot obtain in words, so we propose a novel architecture for combining word representations: character–word embedding based on attention and semantic features. By using an attention mechanism, our method is able to dynamically decide how much information to use from word or character level embedding. With the semantic feature, we can obtain some more information about a word from the sentence. We evaluate different methods on the CEC Corpus, and this method is found to improve performance. </p></blockquote></details> <details> <summary>20. <a href="https://www.aclweb.org/anthology/P18-2066/">Document Embedding Enhanced Event Detection with Hierarchical and Supervised Attention</a> by<i> Yue Zhao, Xiaolong Jin, Yuanzhuo Wang, Xueqi Cheng</i></summary><blockquote><p align="justify"> Document-level information is very important for event detection even at sentence level. In this paper, we propose a novel Document Embedding Enhanced Bi-RNN model, called DEEB-RNN, to detect events in sentences. This model first learns event detection oriented embeddings of documents through a hierarchical and supervised attention based RNN, which pays word-level attention to event triggers and sentence-level attention to those sentences containing events. It then uses the learned document embedding to enhance another bidirectional RNN model to identify event triggers and their types in sentences. Through experiments on the ACE-2005 dataset, we demonstrate the effectiveness and merits of the proposed DEEB-RNN model via comparison with state-of-the-art methods. </p></blockquote></details> <details> <summary>21. <a href="https://www.aclweb.org/anthology/D18-1158/">Collective Event Detection via a Hierarchical and Bias Tagging Networks with Gated Multi-level Attention Mechanisms</a> by<i> Yubo Chen, Hang Yang, Kang Liu, Jun Zhao, Yantao Jia</i> (<a href="https://github.com/yubochen/NBTNGMA4ED">Github</a>)</summary><blockquote><p align="justify"> Traditional approaches to the task of ACE event detection primarily regard multiple events in one sentence as independent ones and recognize them separately by using sentence-level information. However, events in one sentence are usually interdependent and sentence-level information is often insufficient to resolve ambiguities for some types of events. This paper proposes a novel framework dubbed as Hierarchical and Bias Tagging Networks with Gated Multi-level Attention Mechanisms (HBTNGMA) to solve the two problems simultaneously. Firstly, we propose a hierachical and bias tagging networks to detect multiple events in one sentence collectively. Then, we devise a gated multi-level attention to automatically extract and dynamically fuse the sentence-level and document-level information. The experimental results on the widely used ACE 2005 dataset show that our approach significantly outperforms other state-of-the-art methods. </p></blockquote></details> <details> <summary>22. <a href="http://www.nlpr.ia.ac.cn/cip/~liukang/liukangPageFile/Liu_aaai2018.pdf">Event Detection via Gated Multilingual Attention Mechanism</a> by<i> Jian Liu, Yubo Chen1, Kang Liu, Jun Zhao</i></summary><blockquote><p align="justify"> Identifying event instance in text plays a critical role in building NLP applications such as Information Extraction (IE) system. However, most existing methods for this task focus only on monolingual clues of a specific language and ignore the massive information provided by other languages. Data scarcity and monolingual ambiguity hinder the performance of these monolingual approaches. In this paper, we propose a novel multilingual approach — dubbed as Gated MultiLingual Attention (GMLATT) framework — to address the two issues simultaneously. In specific, to alleviate data scarcity problem, we exploit the consistent information in multilingual data via context attention mechanism. Which takes advantage of the consistent evidence in multilingual data other than learning only from monolingual data. To deal with monolingual ambiguity problem, we propose gated cross-lingual attention to exploit the complement information conveyed by multilingual data, which is helpful for the disambiguation. The cross-lingual attention gate serves as a sentinel modelling the confidence of the clues provided by other languages and controls the information integration of various languages. We have conducted extensive experiments on the ACE 2005 benchmark. Experimental results show that our approach significantly outperforms state-of-the-art methods. </p></blockquote></details> <details> <summary>23. <a href="https://link.springer.com/chapter/10.1007/978-3-030-01012-6_21">Prior Knowledge Integrated with Self-attention for Event Detection</a> by<i> Yan Li, Chenliang Li, Weiran Xu, Junliang Li</i></summary><blockquote><p align="justify"> Recently, end-to-end models based on recurrent neural networks (RNN) have gained great success in event detection. However these methods cannot deal with long-distance dependency and internal structure information well. They are also hard to be controlled in process of learning since lacking of prior knowledge integration. In this paper, we present an effective framework for event detection which aims to address these problems. Our model based on self-attention can ignore the distance between any two words to obtain their relationship and leverage internal event argument information to improve event detection. In order to control the process of learning, we first collect keywords from corpus and then use a prior knowledge integration network to encode keywords to a prior knowledge representation. Experimental results demonstrate that our model has significant improvement of 3.9 F1 over the previous state-of-the-art on ACE 2005 dataset. </p></blockquote></details> <details> <summary>24. <a href="https://www.aclweb.org/anthology/P18-1048.pdf">Self-regulation: Employing a Generative Adversarial Network to Improve Event Detection </a> by<i> Tongtao Zhang, Heng Ji and Avirup Sil</i></summary><blockquote><p align="justify">Due to the ability of encoding and map-ping semantic information into a high-dimensional latent feature space, neuralnetworks have been successfully used fordetecting events to a certain extent. How-ever, such a feature space can be easilycontaminated by spurious features inher-ent in event detection. In this paper, wepropose a self-regulated learning approachby utilizing a generative adversarial net-work to generate spurious features. On thebasis, we employ a recurrent network toeliminate the fakes. Detailed experimentson the ACE 2005 and TAC-KBP 2015 cor-pora show that our proposed method ishighly effective and adaptable. </p></blockquote></details> <details> <summary>25. <a href="https://tel.archives-ouvertes.fr/tel-01943841/document/">Neural Methods for Event Extraction </a> by<i> Emanuela Boros</i> </summary><blockquote><p align="justify"> Neural Methods for Event Extraction thesis </p></blockquote></details> ### 2019 <details> <summary>1. <a href="https://arxiv.org/abs/1906.06003">Cost-sensitive Regularization for Label Confusion-aware Event Detection</a> by<i> Hongyu Lin, Yaojie Lu, Xianpei Han, Le Sun</i> (<a href="https://github.com/sanmusunrise/CSR">Github</a>)</summary><blockquote><p align="justify"> In supervised event detection, most of the mislabeling occurs between a small number of confusing type pairs, including trigger-NIL pairs and sibling sub-types of the same coarse type. To address this label confusion problem, this paper proposes cost-sensitive regularization, which can force the training procedure to concentrate more on optimizing confusing type pairs. Specifically, we introduce a cost-weighted term into the training loss, which penalizes more on mislabeling between confusing label pairs. Furthermore, we also propose two estimators which can effectively measure such label confusion based on instance-level or population-level statistics. Experiments on TAC-KBP 2017 datasets demonstrate that the proposed method can significantly improve the performances of different models in both English and Chinese event detection. </p></blockquote></details> <details> <summary>2. <a href="https://link.springer.com/chapter/10.1007/978-3-030-15712-8_51">Exploiting a More Global Context for Event Detection Through Bootstrapping</a> by<i> Dorian Kodelja, Romaric Besançon, Olivier Ferret</i></summary><blockquote><p align="justify"> Over the last few years, neural models for event extraction have obtained interesting results. However, their application is generally limited to sentences, which can be an insufficient scope for disambiguating some occurrences of events. In this article, we propose to integrate into a convolutional neural network the representation of contexts beyond the sentence level. This representation is built following a bootstrapping approach by exploiting an intra-sentential convolutional model. Within the evaluation framework of TAC 2017, we show that our global model significantly outperforms the intra-sentential model while the two models are competitive with the results obtained by TAC 2017 participants. </p></blockquote></details> <details> <summary>3. <a href="https://academic.oup.com/bioinformatics/advance-article-abstract/doi/10.1093/bioinformatics/btz607/5544930?redirectedFrom=fulltext">Context awareness and embedding for biomedical event extraction</a> by<i> Shankai Yan, Ka-Chun Wong</i></summary><blockquote><p align="justify"> Motivation: Biomedical event detection is fundamental for information extraction in molecular biology and biomedical research. The detected events form the central basis for comprehensive biomedical knowledge fusion, facilitating the digestion of massive information influx from literature. Limited by the feature context, the existing event detection models are mostly applicable for a single task. A general and scalable computational model is desiderated for biomedical knowledge management. Results: We consider and propose a bottom-up detection framework to identify the events from recognized arguments. To capture the relations between the arguments, we trained a bi-directional Long Short-Term Memory (LSTM) network to model their context embedding. Leveraging the compositional attributes, we further derived the candidate samples for training event classifiers. We built our models on the datasets from BioNLP Shared Task for evaluations. Our method achieved the average F-scores of 0.81 and 0.92 on BioNLPST-BGI and BioNLPST-BB datasets respectively. Comparing with 7 state-of-the-art methods, our method nearly doubled the existing F-score performance (0.92 vs 0.56) on the BioNLPST-BB dataset. Case studies were conducted to reveal the underlying reasons. Availability: https://github.com/cskyan/evntextrc </p></blockquote></details> <details> <summary>4. <a href="https://www.aclweb.org/anthology/N19-1145/">Biomedical Event Extraction based on Knowledge-driven Tree-LSTM</a> by<i> Diya Li, Lifu Huang, Heng Ji, Jiawei Han</i></summary><blockquote><p align="justify"> Event extraction for the biomedical domain is more challenging than that in the general news domain since it requires broader acquisition of domain-specific knowledge and deeper understanding of complex contexts. To better encode contextual information and external background knowledge, we propose a novel knowledge base (KB)-driven tree-structured long short-term memory networks (Tree-LSTM) framework, incorporating two new types of features: (1) dependency structures to capture wide contexts; (2) entity properties (types and category descriptions) from external ontologies via entity linking. We evaluate our approach on the BioNLP shared task with Genia dataset and achieve a new state-of-the-art result. In addition, both quantitative and qualitative studies demonstrate the advancement of the Tree-LSTM and the external knowledge representation for biomedical event extraction. </p></blockquote></details> <details> <summary>5. <a href="https://aaai.org/ojs/index.php/AAAI/article/view/4649">Exploiting the Ground-Truth: An Adversarial Imitation Based Knowledge Distillation Approach for Event Detection</a> by<i> Jian Liu, Yubo Chen, Kang Liu </i></summary><blockquote><p align="justify"> The ambiguity in language expressions poses a great challenge for event detection. To disambiguate event types, current approaches rely on external NLP toolkits to build knowledge representations. Unfortunately, these approaches work in a pipeline paradigm and suffer from error propagation problem. In this paper, we propose an adversarial imitation based knowledge distillation approach, for the first time, to tackle the challenge of acquiring knowledge from rawsentences for event detection. In our approach, a teacher module is first devised to learn the knowledge representations from the ground-truth annotations. Then, we set up a student module that only takes the raw-sentences as the input. The student module is taught to imitate the behavior of the teacher under the guidance of an adversarial discriminator. By this way, the process of knowledge distillation from rawsentence has been implicitly integrated into the feature encoding stage of the student module. To the end, the enhanced student is used for event detection, which processes raw texts and requires no extra toolkits, naturally eliminating the error propagation problem faced by pipeline approaches. We conduct extensive experiments on the ACE 2005 datasets, and the experimental results justify the effectiveness of our approach. </p></blockquote></details> <details> <summary>6. <a href="http://nlp.cs.rpi.edu/paper/imitation2019.pdf">Joint Entity and Event Extraction with Generative Adversarial Imitation Learning</a> by<i> Tongtao Zhang, Heng Ji, Avirup Sil</i></summary><blockquote><p align="justify"> We propose a new framework for entity and event extraction based on generative adversarial imitation learning-an inverse reinforcement learning method using a generative adversarial network (GAN). We assume that instances and labels yield to various extents of difficulty and the gains and penalties (rewards) are expected to be diverse. We utilize discriminators to estimate proper rewards according to the difference between the labels committed by the ground-truth (expert) and the extractor (agent). Our experiments demonstrate that the proposed framework outperforms state-of-the-art methods. </p></blockquote></details> <details> <summary>7. <a href="https://dl.acm.org/citation.cfm?doid=3308558.3313659">Event Detection using Hierarchical Multi-Aspect Attention</a> by<i> Sneha Mehta, Mohammad Raihanul Islam, Huzefa Rangwala, Naren Ramakrishnan</i> (<a href="https://github.com/sumehta/FBMA">Github</a>)</summary><blockquote><p align="justify"> Classical event encoding and extraction methods rely on fixed dictionaries of keywords and templates or require ground truth labels for phrase/sentences. This hinders widespread application of information encoding approaches to large-scale free form (unstructured) text available on the web. Event encoding can be viewed as a hierarchical task where the coarser level task is event detection, i.e., identification of documents containing a specific event, and where the fine-grained task is one of event encoding, i.e., identifying key phrases, key sentences. Hierarchical models with attention seem like a natural choice for this problem, given their ability to differentially attend to more or less important features when constructing document representations. In this work we present a novel factorized bilinear multi-aspect attention mechanism (FBMA) that attends to different aspects of text while constructing its representation. We find that our approach outperforms state-of-the-art baselines for detecting civil unrest, military action, and non-state actor events from corpora in two different languages. </p></blockquote></details> <details> <summary>8. <a href="https://www.ijcai.org/proceedings/2019/753">Extracting Entities and Events as a Single Task Using a Transition-Based Neural Model</a> by<i> Junchi Zhang, Yanxia Qin, Yue Zhang, Mengchi Liu, Donghong Ji</i></summary><blockquote><p align="justify"> The task of event extraction contains subtasks including detections for entity mentions, event triggers and argument roles. Traditional methods solve them as a pipeline, which does not make use of task correlation for their mutual benefits. There have been recent efforts towards building a joint model for all tasks. However, due to technical challenges, there has not been work predicting the joint output structure as a single task. We build a first model to this end using a neural transition-based framework, incrementally predicting complex joint structures in a state-transition process. Results on standard benchmarks show the benefits of the joint model, which gives the best result in the literature. </p></blockquote></details> <details> <summary>9. <a href="https://link.springer.com/chapter/10.1007%2F978-3-030-32381-3_22">Leveraging Multi-head Attention Mechanism to Improve Event Detection</a> by<i> Meihan Tong, Bin Xu, Lei Hou, Juanzi Li, Shuai Wang</i></summary><blockquote><p align="justify"> Event detection (ED) task aims to automatically identify trigger words from unstructured text. In recent years, neural models with attention mechanism have achieved great success on this task. However, existing attention methods tend to focus on meaningless context words and ignore the semantically rich words, which weakens their ability to recognize trigger words. In this paper, we propose MANN, a multi-head attention mechanism model enhanced by argument knowledge to address the above issues. The multi-head mechanism gives MANN the ability to detect a variety of information in a sentence while argument knowledge acts as a supervisor to further improve the quality of attention. Experimental results show that our approach is significantly superior to existing attention-based models. </p></blockquote></details> <details> <summary>10. <a href="https://ialp2019.com/files/papers/IALP2019_092.pdf">Using Mention Segmentation to Improve Event Detection with Multi-head Attention</a> by<i> Jiali Chen, Yu Hong, Jingli Zhang, and Jianmin Yao</i></summary><blockquote><p align="justify"> Sentence-level event detection (ED) is a task ofdetecting words that describe specific types of events, in-cluding the subtasks of trigger word identification and eventtype classification. Previous work straight forwardly inputs asentence into neural classification models and analyzes deepsemantics of words in the sentence one by one. Relying on the semantics, probabilities of event classes can be predicted foreach word, including the carefully defined ACE event classesand a ”N/A” class(i.e., non-trigger word). The models achieve remarkable successes nowadays. However, our findings show that a natural sentence may posses more than one trigger word and thus entail different types of events. In particular,the closely related information of each event only lies in a unique sentence segment but has nothing to do with other segments. In order to reduce negative influences from noises in other segments, we propose to perform semantics learning for event detection only in the scope of segment instead of the whole sentence. Accordingly, we develop a novel ED method which integrates sentence segmentation into the neural event classification architecture. Bidirectional Long Short-Term Memory (Bi-LSTM) with multi-head attention is used as the classification model. Sentence segmentation is boiled down to a sequence labeling problem, where BERT is used. We combine embeddings, and use them as the input of the neural classification model. The experimental results show that the performance of our method reaches 76.8% and 74.2% F1-scores for trigger identification and event type classification, which outperforms the state-of-the-art </p></blockquote></details> <details> <summary>11. <a href="https://www.aclweb.org/anthology/P19-1429/">Distilling Discrimination and Generalization Knowledge for Event Detection via Delta-Representation Learning</a> by<i> Yaojie Lu, Hongyu Lin, Xianpei Han, Le Sun</i></summary><blockquote><p align="justify"> Event detection systems rely on discrimination knowledge to distinguish ambiguous trigger words and generalization knowledge to detect unseen/sparse trigger words. Current neural event detection approaches focus on trigger-centric representations, which work well on distilling discrimination knowledge, but poorly on learning generalization knowledge. To address this problem, this paper proposes a Delta-learning approach to distill discrimination and generalization knowledge by effectively decoupling, incrementally learning and adaptively fusing event representation. Experiments show that our method significantly outperforms previous approaches on unseen/sparse trigger words, and achieves state-of-the-art performance on both ACE2005 and KBP2017 datasets. </p></blockquote></details> <details> <summary>12. <a href="https://www.aclweb.org/anthology/P19-1471/">Detecting Subevents using Discourse and Narrative Features</a> by<i> Mohammed Aldawsari, Mark Finlayson</i></summary><blockquote><p align="justify"> Recognizing the internal structure of events is a challenging language processing task of great importance for text understanding. We present a supervised model for automatically identifying when one event is a subevent of another. Building on prior work, we introduce several novel features, in particular discourse and narrative features, that significantly improve upon prior state-of-the-art performance. Error analysis further demonstrates the utility of these features. We evaluate our model on the only two annotated corpora with event hierarchies: HiEve and the Intelligence Community corpus. No prior system has been evaluated on both corpora. Our model outperforms previous systems on both corpora, achieving 0.74 BLANC F1 on the Intelligence Community corpus and 0.70 F1 on the HiEve corpus, respectively a 15 and 5 percentage point improvement over previous models. </p></blockquote></details> <details> <summary>13. <a href="https://www.aclweb.org/anthology/P19-1521/">Cost-sensitive Regularization for Label Confusion-aware Event Detection</a> by<i> Hongyu Lin, Yaojie Lu, Xianpei Han, Le Sun</i></summary><blockquote><p align="justify"> In supervised event detection, most of the mislabeling occurs between a small number of confusing type pairs, including trigger-NIL pairs and sibling sub-types of the same coarse type. To address this label confusion problem, this paper proposes cost-sensitive regularization, which can force the training procedure to concentrate more on optimizing confusing type pairs. Specifically, we introduce a cost-weighted term into the training loss, which penalizes more on mislabeling between confusing label pairs. Furthermore, we also propose two estimators which can effectively measure such label confusion based on instance-level or population-level statistics. Experiments on TAC-KBP 2017 datasets demonstrate that the proposed method can significantly improve the performances of different models in both English and Chinese event detection. </p></blockquote></details> <details> <summary>14. <a href="https://www.aclweb.org/anthology/P19-1522/">Exploring Pre-trained Language Models for Event Extraction and Generation</a> by<i> Sen Yang, Dawei Feng, Linbo Qiao, Zhigang Kan, Dongsheng Li</i></summary><blockquote><p align="justify"> Traditional approaches to the task of ACE event extraction usually depend on manually annotated data, which is often laborious to create and limited in size. Therefore, in addition to the difficulty of event extraction itself, insufficient training data hinders the learning process as well. To promote event extraction, we first propose an event extraction model to overcome the roles overlap problem by separating the argument prediction in terms of roles. Moreover, to address the problem of insufficient training data, we propose a method to automatically generate labeled data by editing prototypes and screen out generated samples by ranking the quality. Experiments on the ACE2005 dataset demonstrate that our extraction model can surpass most existing extraction methods. Besides, incorporating our generation method exhibits further significant improvement. It obtains new state-of-the-art results on the event extraction task, including pushing the F1 score of trigger classification to 81.1%, and the F1 score of argument classification to 58.9%. </p></blockquote></details> <details> <summary>15. <a href="https://www.aclweb.org/anthology/D19-1027/">Open Event Extraction from Online Text using a Generative Adversarial Network</a> by<i> Rui Wang, Deyu ZHOU, Yulan He</i></summary><blockquote><p align="justify"> To extract the structured representations of open-domain events, Bayesian graphical models have made some progress. However, these approaches typically assume that all words in a document are generated from a single event. While this may be true for short text such as tweets, such an assumption does not generally hold for long text such as news articles. Moreover, Bayesian graphical models often rely on Gibbs sampling for parameter inference which may take long time to converge. To address these limitations, we propose an event extraction model based on Generative Adversarial Nets, called Adversarial-neural Event Model (AEM). AEM models an event with a Dirichlet prior and uses a generator network to capture the patterns underlying latent events. A discriminator is used to distinguish documents reconstructed from the latent events and the original documents. A byproduct of the discriminator is that the features generated by the learned discriminator network allow the visualization of the extracted events. Our model has been evaluated on two Twitter datasets and a news article dataset. Experimental results show that our model outperforms the baseline approaches on all the datasets, with more significant improvements observed on the news article dataset where an increase of 15\% is observed in F-measure. </p></blockquote></details> <details> <summary>16. <a href="https://www.aclweb.org/anthology/D19-1030/">Cross-lingual Structure Transfer for Relation and Event Extraction</a> by<i> Ananya Subburathinam, Di Lu, Heng Ji, Jonathan May, Shih-Fu Chang, Avirup Sil, Clare Voss</i></summary><blockquote><p align="justify"> The identification of complex semantic structures such as events and entity relations, already a challenging Information Extraction task, is doubly difficult from sources written in under-resourced and under-annotated languages. We investigate the suitability of cross-lingual structure transfer techniques for these tasks. We exploit relation and event-relevant language-universal features, leveraging both symbolic (including part-of-speech and dependency path) and distributional (including type representation and contextualized representation) information. By representing all entity mentions, event triggers, and contexts into this complex and structured multilingual common space, using graph convolutional networks, we can train a relation or event extractor from source language annotations and apply it to the target language. Extensive experiments on cross-lingual relation and event transfer among English, Chinese, and Arabic demonstrate that our approach achieves performance comparable to state-of-the-art supervised models trained on up to 3,000 manually annotated mentions: up to 62.6% F-score for Relation Extraction, and 63.1% F-score for Event Argument Role Labeling. The event argument role labeling model transferred from English to Chinese achieves similar performance as the model trained from Chinese. We thus find that language-universal symbolic and distributional representations are complementary for cross-lingual structure transfer. </p></blockquote></details> <details> <summary>17. <a href="https://www.aclweb.org/anthology/D19-1033/">Event Detection with Trigger-Aware Lattice Neural Network</a> by<i> Ning Ding, Ziran Li, Zhiyuan Liu, Haitao Zheng, Zibo Lin</i> (<a href="https://github.com/thunlp/TLNN">Github</a>)</summary><blockquote><p align="justify"> Event detection (ED) aims to locate trigger words in raw text and then classify them into correct event types. In this task, neural net- work based models became mainstream in recent years. However, two problems arise when it comes to languages without natural delimiters, such as Chinese. First, word-based models severely suffer from the problem of word trigger mismatch, limiting the performance of the methods. In addition, even if trigger words could be accurately located, the ambiguity of polysemy of triggers could still affect the trigger classification stage. To address the two issues simultaneously, we propose the Trigger-aware Lattice Neural Net- work (TLNN). (1) The framework dynamically incorporates word and character information so that the trigger-word mismatch issue can be avoided. (2) Moreover, for polysemous characters and words, we model all senses of them with the help of an external linguistic knowledge base, so as to alleviate the problem of ambiguous triggers. Experiments on two benchmark datasets show that our model could effectively tackle the two issues and outperforms previous state-of-the-art methods significantly, giving the best results. The source code of this paper can be obtained from https://github.com/thunlp/TLNN. </p></blockquote></details> <details> <summary>18. <a href="https://www.aclweb.org/anthology/D19-1041/">Joint Event and Temporal Relation Extraction with Shared Representations and Structured Prediction</a> by<i> Rujun Han, Qiang Ning, Nanyun Peng</i> (<a href="https://github.com/rujunhan/EMNLP-2019">Github</a>)</summary><blockquote><p align="justify"> We propose a joint event and temporal relation extraction model with shared representation learning and structured prediction. The proposed method has two advantages over existing work. First, it improves event representation by allowing the event and relation modules to share the same contextualized embeddings and neural representation learner. Second, it avoids error propagation in the conventional pipeline systems by leveraging structured inference and learning methods to assign both the event labels and the temporal relation labels jointly. Experiments show that the proposed method can improve both event extraction and temporal relation extraction over state-of-the-art systems, with the end-to-end F1 improved by 10% and 6.8% on two benchmark datasets respectively. </p></blockquote></details> <details> <summary>19. <a href="https://www.aclweb.org/anthology/D19-1584/">HMEAE: Hierarchical Modular Event Argument Extraction</a> by<i> Xiaozhi Wang, Ziqi Wang, Xu Han, Zhiyuan Liu, Juanzi Li, Peng Li, Maosong Sun, Jie Zhou, Xiang Ren</i> (<a href="https://github.com/thunlp/HMEAE">Github</a>)</summary><blockquote><p align="justify"> Existing event extraction methods classify each argument role independently, ignoring the conceptual correlations between different argument roles. In this paper, we propose a Hierarchical Modular Event Argument Extraction (HMEAE) model, to provide effective inductive bias from the concept hierarchy of event argument roles. Specifically, we design a neural module network for each basic unit of the concept hierarchy, and then hierarchically compose relevant unit modules with logical operations into a role-oriented modular network to classify a specific argument role. As many argument roles share the same high-level unit module, their correlation can be utilized to extract specific event arguments better. Experiments on real-world datasets show that HMEAE can effectively leverage useful knowledge from the concept hierarchy and significantly outperform the state-of-the-art baselines. The source code can be obtained from https://github.com/thunlp/HMEAE. </p></blockquote></details> <details> <summary>20. <a href="https://www.aclweb.org/anthology/D19-1585/">Entity, Relation, and Event Extraction with Contextualized Span Representations</a> by<i> David Wadden, Ulme Wennberg, Yi Luan, Hannaneh Hajishirzi</i> (<a href="https://github.com/dwadden/dygiepp">Github</a>)</summary><blockquote><p align="justify"> We examine the capabilities of a unified, multi-task framework for three information extraction tasks: named entity recognition, relation extraction, and event extraction. Our framework (called DyGIE++) accomplishes all tasks by enumerating, refining, and scoring text spans designed to capture local (within-sentence) and global (cross-sentence) context. Our framework achieves state-of-the-art results across all tasks, on four datasets from a variety of domains. We perform experiments comparing different techniques to construct span representations. Contextualized embeddings like BERT perform well at capturing relationships among entities in the same or adjacent sentences, while dynamic span graph updates model long-range cross-sentence relationships. For instance, propagating span representations via predicted coreference links can enable the model to disambiguate challenging entity mentions. Our code is publicly available at this https URL and can be easily adapted for new tasks or datasets. </p></blockquote></details> <details> <summary>21. <a href="https://www.aclweb.org/anthology/D19-1582/">Event Detection with Multi-Order Graph Convolution and Aggregated Attention</a> by<i> Haoran Yan, Xiaolong Jin, Xiangbin Meng, Jiafeng Guo, Xueqi Cheng</i> (<a href="https://github.com/ll0iecas/MOGANED">Github TensorFlow Unofficial</a>, <a href="https://github.com/wzq016/MOGANED-Implementation">Github Pytorch Unofficial</a>)</summary><blockquote><p align="justify"> Syntactic relations are broadly used in many NLP tasks. For event detection, syntactic relation representations based on dependency tree can better capture the interrelations between candidate trigger words and related entities than sentence representations. But, existing studies only use first-order syntactic relations (i.e., the arcs) in dependency trees to identify trigger words. For this reason, this paper proposes a new method for event detection, which uses a dependency tree based graph convolution network with aggregative attention to explicitly model and aggregate multi-order syntactic representations in sentences. Experimental comparison with state-of-the-art baselines shows the superiority of the proposed method. </p></blockquote></details>* <details> <summary>22. <a href="https://ieeexplore.ieee.org/document/8852355">GADGET: Using Gated GRU for Biomedical Event Trigger Detection</a> by<i> Cheng Zeng ; Yi Zhang ; Heng-Yang Lu ; Chong-Jun Wang </i></summary><blockquote><p align="justify"> Biomedical event extraction plays an important role in the field of biomedical text mining, and the event trigger detection is the first step in the pipeline process of event extraction. Event trigger can clearly indicates the occurrence of related events. There have been many machine learning based methods applied to this area already. However, most previous work have omitted two crucial points: (1) Class Difference: They simply regard non-trigger as same level class label. (2) Information Isolation: Most methods only utilize token level information. In this paper, we propose a novel model based on gate mechanism, which identifies trigger and non-trigger words in the first stage. At the same time, we also introduce additional fusion layer in order to incorporate sentence level information for event trigger detection. Experimental results on the Multi Level Event Extraction (MLEE) corpus achieve superior performance than other state-of-the-art models. We have also performed ablation study to show the effectiveness of proposed model components. </p></blockquote></details> <details> <summary>23. <a href="https://arxiv.org/abs/1910.11621">Meta-Learning with Dynamic-Memory-Based Prototypical Network for Few-Shot Event Detection</a> by<i> Shumin Deng, Ningyu Zhang, Jiaojian Kang, Yichi Zhang, Wei Zhang, Huajun Chen </i></summary><blockquote><p align="justify"> Event detection (ED), a sub-task of event extraction, involves identifying triggers and categorizing event mentions. Existing methods primarily rely upon supervised learning and require large-scale labeled event datasets which are unfortunately not readily available in many real-life applications. In this paper, we consider and reformulate the ED task with limited labeled data as a Few-Shot Learning problem. We propose a Dynamic-Memory-Based Prototypical Network (DMB-PN), which exploits Dynamic Memory Network (DMN) to not only learn better prototypes for event types, but also produce more robust sentence encodings for event mentions. Differing from vanilla prototypical networks simply computing event prototypes by averaging, which only consume event mentions once, our model is more robust and is capable of distilling contextual information from event mentions for multiple times due to the multi-hop mechanism of DMNs. The experiments show that DMB-PN not only deals with sample scarcity better than a series of baseline models but also performs more robustly when the variety of event types is relatively large and the instance quantity is extremely small. </p></blockquote></details> <details> <summary>24. <a href="https://www.mitpressjournals.org/doi/full/10.1162/dint_a_00014">Joint entity and event extraction with generative adversarial imitation learning</a> by<i> Tongtao Zhang, Heng Ji and Avirup Sil</i></summary><blockquote><p align="justify"> We propose a new framework for entity and event extraction based on generative adversarial imitation learning—an inverse reinforcement learning method using a generative adversarial network (GAN). We assume that instances and labels yield to various extents of difficulty and the gains and penalties (rewards) are expected to be diverse. We utilize discriminators to estimate proper rewards according to the difference between the labels committed by the ground-truth (expert) and the extractor (agent). Our experiments demonstrate that the proposed framework outperforms state-of-the-art methods. </p></blockquote></details> ## 2020 <details> <summary>1. <a href="https://arxiv.org/abs/2002.10757">Event Detection with Relation-Aware Graph Convolutional Neural Networks</a> by<i> Shiyao Cui, Bowen Yu, Tingwen Liu, Zhenyu Zhang, Xuebin Wang, Jinqiao Shi</i></summary><blockquote><p align="justify"> Event detection (ED), a key subtask of information extraction, aims to recognize instances of specific types of events in text. Recently, graph convolutional networks (GCNs) over dependency trees have been widely used to capture syntactic structure information and get convincing performances in event detection. However, these works ignore the syntactic relation labels on the tree, which convey rich and useful linguistic knowledge for event detection. In this paper, we investigate a novel architecture named Relation-Aware GCN (RA-GCN), which efficiently exploits syntactic relation labels and models the relation between words specifically. We first propose a relation-aware aggregation module to produce expressive word representation by aggregating syntactically connected words through specific relation. Furthermore, a context-aware relation update module is designed to explicitly update the relation representation between words, and these two modules work in the mutual promotion way. Experimental results on the ACE2005 dataset show that our model achieves a new state-of-the-art performance for event detection. </p></blockquote></details> <details> <summary>2. <a href="https://www.aclweb.org/anthology/2020.lrec-1.273">A Platform for Event Extraction in Hindi</a> by<i> Sovan Kumar Sahoo, Saumajit Saha, Asif Ekbal, Pushpak Bhattacharyya</i></summary><blockquote><p align="justify"> Event Extraction is an important task in the widespread field of Natural Language Processing (NLP). Though this task is adequately addressed in English with sufficient resources, we are unaware of any benchmark setup in Indian languages. Hindi is one of the most widely spoken languages in the world. In this paper, we present an Event Extraction framework for Hindi language by creating an annotated resource for benchmarking, and then developing deep learning based models to set as the baselines. We crawl more than seventeen hundred disaster related Hindi news articles from the various news sources. We also develop deep learning based models for Event Trigger Detection and Classification, Argument Detection and Classification and Event-Argument Linking. </p></blockquote></details> <details> <summary>3. <a href="https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-3376-2">Biomedical event extraction with a novel combination strategy based on hybrid deep neural networks</a> by<i> Lvxing Zhu, Haoran Zheng</i></summary><blockquote><p align="justify"> Background Biomedical event extraction is a fundamental and in-demand technology that has attracted substantial interest from many researchers. Previous works have heavily relied on manual designed features and external NLP packages in which the feature engineering is large and complex. Additionally, most of the existing works use the pipeline process that breaks down a task into simple sub-tasks but ignores the interaction between them. To overcome these limitations, we propose a novel event combination strategy based on hybrid deep neural networks to settle the task in a joint end-to-end manner. Results We adapted our method to several annotated corpora of biomedical event extraction tasks. Our method achieved state-of-the-art performance with noticeable overall F1 score improvement compared to that of existing methods for all of these corpora. Conclusions The experimental results demonstrated that our method is effective for biomedical event extraction. The combination strategy can reconstruct complex events from the output of deep neural networks, while the deep neural networks effectively capture the feature representation from the raw text. The biomedical event extraction implementation is available online at http://www.predictor.xin/event_extraction. </p></blockquote></details> <details> <summary>4. <a href="https://www.aclweb.org/anthology/2020.lrec-1.244/">Cross-Domain Evaluation of Edge Detection for Biomedical Event Extraction</a> by<i> Alan Ramponi, Barbara Plank, Rosario Lombardo</i></summary><blockquote><p align="justify"> Biomedical event extraction is a crucial task in order to automatically extract information from the increasingly growing body of biomedical literature. Despite advances in the methods in recent years, most event extraction systems are still evaluated in-domain and on complete event structures only. This makes it hard to determine the performance of intermediate stages of the task, such as edge detection, across different corpora. Motivated by these limitations, we present the first cross-domain study of edge detection for biomedical event extraction. We analyze differences between five existing gold standard corpora, create a standardized benchmark corpus, and provide a strong baseline model for edge detection. Experiments show a large drop in performance when the baseline is applied on out-of-domain data, confirming the need for domain adaptation methods for the task. To encourage research efforts in this direction, we make both the data and the baseline available to the research community: https://www.cosbi.eu/cfx/9985. </p></blockquote></details> <details> <summary>5. <a href="https://www.aclweb.org/anthology/2020.lrec-1.243/">Cross-lingual Structure Transfer for Zero-resource Event Extraction</a> by<i> Di Lu, Ananya Subburathinam, Heng Ji, Jonathan May, Shih-Fu Chang, Avi Sil, Clare Voss</i></summary><blockquote><p align="justify"> Most of the current cross-lingual transfer learning methods for Information Extraction (IE) have been only applied to name tagging. To tackle more complex tasks such as event extraction we need to transfer graph structures (event trigger linked to multiple arguments with various roles) across languages. We develop a novel share-and-transfer framework to reach this goal with three steps: (1) Convert each sentence in any language to language-universal graph structures; in this paper we explore two approaches based on universal dependency parses and complete graphs, respectively. (2) Represent each node in the graph structure with a cross-lingual word embedding so that all sentences in multiple languages can be represented with one shared semantic space. (3) Using this common semantic space, train event extractors from English training data and apply them to languages that do not have any event annotations. Experimental results on three languages (Spanish, Russian and Ukrainian) without any annotations show this framework achieves comparable performance to a state-of-the-art supervised model trained from more than 1,500 manually annotated event mentions. </p></blockquote></details> <details> <summary>6. <a href="https://arxiv.org/abs/2004.13625">Event Extraction by Answering (Almost) Natural Questions</a> by<i> Xinya Du, Claire Cardie</i> (<a href="https://github.com/xinyadu/eeqa">Github</a>)</summary><blockquote><p align="justify"> The problem of event extraction requires detecting the event trigger and extracting its corresponding arguments. Existing work in event argument extraction typically relies heavily on entity recognition as a preprocessing/concurrent step, causing the well-known problem of error propagation. To avoid this issue, we introduce a new paradigm for event extraction by formulating it as a question answering (QA) task, which extracts the event arguments in an end-to-end manner. Empirical results demonstrate that our framework outperforms prior methods substantially; in addition, it is capable of extracting event arguments for roles not seen at training time (zero-shot learning setting). </p></blockquote></details> <details> <summary>7. <a href="https://www.aclweb.org/anthology/2020.lrec-1.216/">Towards Few-Shot Event Mention Retrieval: An Evaluation Framework and A Siamese Network Approach</a> by<i> Bonan Min, Yee Seng Chan, Lingjun Zhao</i></summary><blockquote><p align="justify"> Automatically analyzing events in a large amount of text is crucial for situation awareness and decision making. Previous approaches treat event extraction as “one size fits all” with an ontology defined a priori. The resulted extraction models are built just for extracting those types in the ontology. These approaches cannot be easily adapted to new event types nor new domains of interest. To accommodate personalized event-centric information needs, this paper introduces the few-shot Event Mention Retrieval (EMR) task: given a user-supplied query consisting of a handful of event mentions, return relevant event mentions found in a corpus. This formulation enables “query by example”, which drastically lowers the bar of specifying event-centric information needs. The retrieval setting also enables fuzzy search. We present an evaluation framework leveraging existing event datasets such as ACE. We also develop a Siamese Network approach, and show that it performs better than ad-hoc retrieval models in the few-shot EMR setting. </p></blockquote></details> <details> <summary>8. <a href="https://www.aclweb.org/anthology/2020.acl-main.522/">Improving Event Detection via Open-domain Trigger Knowledge</a> by<i> Meihan Tong, Bin Xu, Shuai Wang, Yixin Cao, Lei Hou, Juanzi Li, Jun Xie</i></summary><blockquote><p align="justify"> Event Detection (ED) is a fundamental task in automatically structuring texts. Due to the small scale of training data, previous methods perform poorly on unseen/sparsely labeled trigger words and are prone to overfitting densely labeled trigger words. To address the issue, we propose a novel Enrichment Knowledge Distillation (EKD) model to leverage external open-domain trigger knowledge to reduce the in-built biases to frequent trigger words in annotations. Experiments on benchmark ACE2005 show that our model outperforms nine strong baselines, is especially effective for unseen/sparsely labeled trigger words. The source code is released on https://github.com/shuaiwa16/ekd.git. </p></blockquote></details> <details> <summary>9. <a href="https://www.aclweb.org/anthology/2020.acl-main.667/">A Two-Step Approach for Implicit Event Argument Detection</a> by<i> Zhisong Zhang, Xiang Kong, Zhengzhong Liu, Xuezhe Ma, Eduard Hovy</i></summary><blockquote><p align="justify"> In this work, we explore the implicit event argument detection task, which studies event arguments beyond sentence boundaries. The addition of cross-sentence argument candidates imposes great challenges for modeling. To reduce the number of candidates, we adopt a two-step approach, decomposing the problem into two sub-problems: argument head-word detection and head-to-span expansion. Evaluated on the recent RAMS dataset (Ebner et al., 2020), our model achieves overall better performance than a strong sequence labeling baseline. We further provide detailed error analysis, presenting where the model mainly makes errors and indicating directions for future improvements. It remains a challenge to detect implicit arguments, calling for more future work of document-level modeling for this task. </p></blockquote></details> <details> <summary>10. <a href="https://www.aclweb.org/anthology/2020.nuse-1.5/">Extensively Matching for Few-shot Learning Event Detection</a> by<i> Viet Dac Lai, Thien Huu Nguyen, Franck Dernoncourt</i></summary><blockquote><p align="justify"> Current event detection models under supervised learning settings fail to transfer to new event types. Few-shot learning has not been explored in event detection even though it allows a model to perform well with high generalization on new event types. In this work, we formulate event detection as a few-shot learning problem to enable to extend event detection to new event types. We propose two novel loss factors that matching examples in the support set to provide more training signals to the model. Moreover, these training signals can be applied in many metric-based few-shot learning models. Our extensive experiments on the ACE-2005 dataset (under a few-shot learning setting) show that the proposed method can improve the performance of few-shot learning. </p></blockquote></details> <details> <summary>11. <a href="https://www.aclweb.org/anthology/2020.acl-main.714/">Document-Level Event Role Filler Extraction using Multi-Granularity Contextualized Encoding</a> by<i> Xinya Du, Claire Cardie</i></summary><blockquote><p align="justify"> Few works in the literature of event extraction have gone beyond individual sentences to make extraction decisions. This is problematic when the information needed to recognize an event argument is spread across multiple sentences. We argue that document-level event extraction is a difficult task since it requires a view of a larger context to determine which spans of text correspond to event role fillers. We first investigate how end-to-end neural sequence models (with pre-trained language model representations) perform on document-level role filler extraction, as well as how the length of context captured affects the models’ performance. To dynamically aggregate information captured by neural representations learned at different levels of granularity (e.g., the sentence- and paragraph-level), we propose a novel multi-granularity reader. We evaluate our models on the MUC-4 event extraction dataset, and show that our best system performs substantially better than prior work. We also report findings on the relationship between context length and neural model performance on the task. </p></blockquote></details> <details> <summary>12. <a href="https://www.aclweb.org/anthology/2020.bionlp-1.21/">Global Locality in Biomedical Relation and Event Extraction</a> by<i> Elaheh ShafieiBavani, Antonio Jimeno Yepes, Xu Zhong, David Martinez Iraola</i></summary><blockquote><p align="justify"> Due to the exponential growth of biomedical literature, event and relation extraction are important tasks in biomedical text mining. Most work only focus on relation extraction, and detect a single entity pair mention on a short span of text, which is not ideal due to long sentences that appear in biomedical contexts. We propose an approach to both relation and event extraction, for simultaneously predicting relationships between all mention pairs in a text. We also perform an empirical study to discuss different network setups for this purpose. The best performing model includes a set of multi-head attentions and convolutions, an adaptation of the transformer architecture, which offers self-attention the ability to strengthen dependencies among related elements, and models the interaction between features extracted by multiple attention heads. Experiment results demonstrate that our approach outperforms the state of the art on a set of benchmark biomedical corpora including BioNLP 2009, 2011, 2013 and BioCreative 2017 shared tasks. </p></blockquote></details> <details> <summary>13. <a href="https://www.aclweb.org/anthology/2020.acl-main.681/">Towards Open Domain Event Trigger Identification using Adversarial Domain Adaptation</a> by<i> Aakanksha Naik, Carolyn Rose</i></summary><blockquote><p align="justify"> We tackle the task of building supervised event trigger identification models which can generalize better across domains. Our work leverages the adversarial domain adaptation (ADA) framework to introduce domain-invariance. ADA uses adversarial training to construct representations that are predictive for trigger identification, but not predictive of the example’s domain. It requires no labeled data from the target domain, making it completely unsupervised. Experiments with two domains (English literature and news) show that ADA leads to an average F1 score improvement of 3.9 on out-of-domain data. Our best performing model (BERT-A) reaches 44-49 F1 across both domains, using no labeled target data. Preliminary experiments reveal that finetuning on 1% labeled data, followed by self-training leads to substantial improvement, reaching 51.5 and 67.2 F1 on literature and news respectively. </p></blockquote></details> <details> <summary>14. <a href="https://www.aclweb.org/anthology/2020.acl-main.230/">Cross-media Structured Common Space for Multimedia Event Extraction</a> by<i> Manling Li, Alireza Zareian, Qi Zeng, Spencer Whitehead, Di Lu, Heng Ji, Shih-Fu Chang</i></summary><blockquote><p align="justify"> We introduce a new task, MultiMedia Event Extraction, which aims to extract events and their arguments from multimedia documents. We develop the first benchmark and collect a dataset of 245 multimedia news articles with extensively annotated events and arguments. We propose a novel method, Weakly Aligned Structured Embedding (WASE), that encodes structured representations of semantic information from textual and visual data into a common embedding space. The structures are aligned across modalities by employing a weakly supervised training strategy, which enables exploiting available resources without explicit cross-media annotation. Compared to uni-modal state-of-the-art methods, our approach achieves 4.0% and 9.8% absolute F-score gains on text event argument role labeling and visual event extraction. Compared to state-of-the-art multimedia unstructured representations, we achieve 8.3% and 5.0% absolute F-score gains on multimedia event extraction and argument role labeling, respectively. By utilizing images, we extract 21.4% more event mentions than traditional text-only methods. </p></blockquote></details> <details> <summary>15. <a href="https://www.aclweb.org/anthology/2020.acl-srw.23/">Exploring Interpretability in Event Extraction: Multitask Learning of a Neural Event Classifier and an Explanation Decoder</a> by<i> Zheng Tang, Gus Hahn-Powell, Mihai Surdeanu</i></summary><blockquote><p align="justify"> We propose an interpretable approach for event extraction that mitigates the tension between generalization and interpretability by jointly training for the two goals. Our approach uses an encoder-decoder architecture, which jointly trains a classifier for event extraction, and a rule decoder that generates syntactico-semantic rules that explain the decisions of the event classifier. We evaluate the proposed approach on three biomedical events and show that the decoder generates interpretable rules that serve as accurate explanations for the event classifier’s decisions, and, importantly, that the joint training generally improves the performance of the event classifier. Lastly, we show that our approach can be used for semi-supervised learning, and that its performance improves when trained on automatically-labeled data generated by a rule-based system. </p></blockquote></details> <details> <summary>14. <a href="https://aaai.org/ojs/index.php/AAAI/article/view/6437"> Image Enhanced Event Detection in News Articles</a> by<i> Meihan Tong, Shuai Wang, Yixin Cao, Bin Xu, Juanzi Li, Lei Hou and Tat-Seng Chua </i></summary><blockquote><p align="justify"> Event detection is a crucial and challenging sub-task of event extraction, which suffers from a severe ambiguity issue of trigger words. Existing works mainly focus on using textual context information, while there naturally exist many images accompanied by news articles that are yet to be explored. We believe that images not only reflect the core events of the text, but are also helpful for the disambiguation of trigger words. In this paper, we first contribute an image dataset supplement to ED benchmarks (i.e., ACE2005) for training and evaluation. We then propose a novel Dual Recurrent Multimodal Model, DRMM, to conduct deep interactions between images and sentences for modality features aggregation. DRMM utilizes pre-trained BERT and ResNet to encode sentences and images, and employs an alternating dual attention to select informative features for mutual enhancements. Our superior performance compared to six state-of-art baselines as well as further ablation studies demonstrate the significance of image modality and effectiveness of the proposed architecture. The code and image dataset are avaliable at https://github.com/shuaiwa16/image-enhanced-event-extraction. </p></blockquote></details> <details> <summary>15. <a href="https://ebiquity.umbc.edu/paper/html/id/874/CASIE-Extracting-Cybersecurity-Event-Information-from-Text"> CASIE: Extracting Cybersecurity Event Information from Text</a> by<i> Taneeya W. Satyapanich, Francis Ferraro, and Tim Finin </i> (<a href="https://github.com/Ebiquity/CASIE">Github</a>)</summary><blockquote><p align="justify"> We present CASIE, a system that extracts information about cybersecurity events from text and populates a semantic model, with the ultimate goal of integration into a knowledge graph of cybersecurity data. It was trained on a new corpus of ~1,000 English news articles from 2017--2019 that are labeled with rich, event-based annotations and that covers both cyberattack and vulnerability-related events. Our model defines five event subtypes along with their semantic roles and 20 event-relevant argument types (e.g., file, device, software, money). CASIE uses different deep neural networks approaches with attention and can incorporate rich linguistic features and word embeddings. We have conducted experiments on each component in the event detection pipeline and the results show that each subsystem performs well. </p></blockquote></details> <details> <summary>16. <a href="https://arxiv.org/abs/1910.11621"> Meta-Learning with Dynamic-Memory-Based Prototypical Network for Few-Shot Event Detection</a> by<i> Shumin Deng, Ningyu Zhang, Jiaojian Kang, Yichi Zhang, Wei Zhang, Huajun Chen </i> (<a href="https://github.com/231sm/Low_Resource_KBP">Github</a>)</summary><blockquote><p align="justify"> Event detection (ED), a sub-task of event extraction, involves identifying triggers and categorizing event mentions. Existing methods primarily rely upon supervised learning and require large-scale labeled event datasets which are unfortunately not readily available in many real-life applications. In this paper, we consider and reformulate the ED task with limited labeled data as a Few-Shot Learning problem. We propose a Dynamic-Memory-Based Prototypical Network (DMB-PN), which exploits Dynamic Memory Network (DMN) to not only learn better prototypes for event types, but also produce more robust sentence encodings for event mentions. Differing from vanilla prototypical networks simply computing event prototypes by averaging, which only consume event mentions once, our model is more robust and is capable of distilling contextual information from event mentions for multiple times due to the multi-hop mechanism of DMNs. The experiments show that DMB-PN not only deals with sample scarcity better than a series of baseline models but also performs more robustly when the variety of event types is relatively large and the instance quantity is extremely small. </p></blockquote></details> <details> <summary>17. <a href="https://arxiv.org/abs/2002.05295"> Exploiting the Matching Information in the Support Set for Few Shot Event Classificationt</a> by<i> Viet Dac Lai, Franck Dernoncourt, Thien Huu Nguyen </i></summary><blockquote><p align="justify"> The existing event classification (EC) work primarily focuseson the traditional supervised lea
gitextract_9gttryg9/ ├── LICENSE └── README.md
Condensed preview — 2 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (379K chars).
[
{
"path": "LICENSE",
"chars": 1072,
"preview": "MIT License\n\nCopyright (c) 2019 Baptiste Blouin\n\nPermission is hereby granted, free of charge, to any person obtaining a"
},
{
"path": "README.md",
"chars": 374352,
"preview": "# Event Extraction papers\n\nThis repository contains resources for Natural Language Processing (NLP) with a focus on the "
}
]
About this extraction
This page contains the full source code of the BaptisteBlouin/EventExtractionPapers GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 2 files (366.6 KB), approximately 79.4k tokens. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.