Repository: jeniyat/StackOverflowNER Branch: master Commit: d1f408d235fc Files: 66 Total size: 82.7 MB Directory structure: gitextract_71vcekvo/ ├── License ├── Readme.md ├── code/ │ ├── Attentive_BiLSTM/ │ │ ├── HAN.py │ │ ├── Word_Freqency_Mapper.py │ │ ├── auxilary_inputs_ner/ │ │ │ ├── ctc_pred.tsv │ │ │ └── segmenter_pred/ │ │ │ ├── segmenter_pred_dev.txt │ │ │ ├── segmenter_pred_test.txt │ │ │ └── segmenter_pred_train.txt │ │ ├── config_so.py │ │ ├── conlleval_py.py │ │ ├── evaluation/ │ │ │ └── conlleval │ │ ├── gaussian_binner.py │ │ ├── loader_so.py │ │ ├── make_segment_pred.py │ │ ├── make_vocab.py │ │ ├── model.py │ │ ├── other_files/ │ │ │ ├── Freq_Vector.txt │ │ │ ├── oov_words.txt │ │ │ └── vocab.tsv │ │ ├── print_result.py │ │ ├── sorted_entity_list_by_count_all.json │ │ ├── test_char_embeddings.py │ │ ├── test_script.py │ │ ├── tolatex.py │ │ ├── train_so.py │ │ └── utils_so.py │ ├── BERT_NER/ │ │ ├── E2E_SoftNER.py │ │ ├── Freq_Vector.txt │ │ ├── softner_ner_predict_from_file.py │ │ ├── softner_segmenter_preditct_from_file.py │ │ ├── utils_ctc/ │ │ │ ├── binning.py │ │ │ ├── config_ctc.py │ │ │ ├── features.py │ │ │ ├── model.py │ │ │ ├── prediction_ctc.py │ │ │ └── rules.py │ │ ├── utils_ner.py │ │ ├── utils_preprocess/ │ │ │ ├── __init__.py │ │ │ ├── anntoconll.py │ │ │ ├── fix_char_encoding.py │ │ │ ├── format_markdown.py │ │ │ ├── map_text_to_char.py │ │ │ ├── sentencesplit.py │ │ │ ├── ssplit.py │ │ │ ├── stokenizer.py │ │ │ ├── stokenizer_base_rules.py │ │ │ └── tokenize_base_rules.py │ │ ├── utils_seg.py │ │ └── xml_filted_body.txt │ ├── DataReader/ │ │ ├── Posts_Small.xml │ │ ├── loader_so.py │ │ ├── read_so_post_info.py │ │ ├── temp_xml.xml │ │ └── text_files/ │ │ ├── 13347179.txt │ │ ├── 13352832.txt │ │ └── 1528_1533.txt │ ├── Readme.md │ └── SOTokenizer/ │ ├── ark_twokenize.py │ └── stokenizer.py └── resources/ ├── annotated_ner_data/ │ ├── GitHub/ │ │ └── GH_test_set.txt │ ├── Readme.md │ └── StackOverflow/ │ ├── dev.txt │ ├── test.txt │ ├── train.txt │ └── train_merged_labels.txt └── pretrained_word_vectors/ └── Readme.md ================================================ FILE CONTENTS ================================================ ================================================ FILE: License ================================================ MIT License Copyright (c) 2020 JeniyaTabassum 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 ================================================ # Dataset and Model for Fine-grained Software Entity Extraction This repository contains all the code and data proposed in the paper: **Code and Named Entity Recognition in StackOverflow. (ACL 2020)**. [[Paper PDF](https://arxiv.org/pdf/2005.01634.pdf)] For the source code of our NER tagger, check the `code/NER/` folder. For our annotated data with software-domain named entities, check the `resources/annotated_ner_data/` folder. To cite the data or the code included in this repository, please use the following bibtex entry: @inproceedings{Tabassum20acl, title = {Code and Named Entity Recognition in StackOverflow}, author = "Tabassum, Jeniya and Maddela, Mounica and Xu, Wei and Ritter, Alan", booktitle = {The Annual Meeting of the Association for Computational Linguistics (ACL)}, year = {2020} } ================================================ FILE: code/Attentive_BiLSTM/HAN.py ================================================ import torch import torch.nn as nn import torch.autograd as autograd import torch.nn.functional as F from utils_so import * from config_so import parameters torch.backends.cudnn.deterministic = True torch.manual_seed(parameters["seed"]) class Embeeding_Attn(nn.Module): def __init__(self): super(Embeeding_Attn, self).__init__() self.max_len = 3 self.input_dim = 1824 self.hidden_dim = 150 self.bidirectional = True self.drop_out_rate = 0.5 self.context_vector_size = [parameters['embedding_context_vecotr_size'], 1] self.drop = nn.Dropout(p=self.drop_out_rate) self.word_GRU = nn.GRU(input_size=self.input_dim, hidden_size=self.hidden_dim, bidirectional=self.bidirectional, batch_first=True) self.w_proj = nn.Linear(in_features=2*self.hidden_dim ,out_features=2*self.hidden_dim) self.w_context_vector = nn.Parameter(torch.randn(self.context_vector_size).float()) self.softmax = nn.Softmax(dim=1) init_gru(self.word_GRU) def forward(self,x): x, _ = self.word_GRU(x) Hw = torch.tanh(self.w_proj(x)) w_score = self.softmax(Hw.matmul(self.w_context_vector)) x = x.mul(w_score) x = torch.sum(x, dim=1) return x class Word_Attn(nn.Module): def __init__(self): super(Word_Attn, self).__init__() self.max_len = 92 self.input_dim = 300 self.hidden_dim = 150 self.bidirectional = True self.drop_out_rate = 0.5 self.context_vector_size = [parameters['word_context_vecotr_size'] , 1] self.drop = nn.Dropout(p=self.drop_out_rate) self.word_GRU = nn.GRU(input_size=self.input_dim, hidden_size=self.hidden_dim, bidirectional=self.bidirectional, batch_first=True) self.w_proj = nn.Linear(in_features=2*self.hidden_dim ,out_features=2*self.hidden_dim) self.w_context_vector = nn.Parameter(torch.randn(self.context_vector_size).float()) self.softmax = nn.Softmax(dim=1) init_gru(self.word_GRU) def forward(self,x): x, _ = self.word_GRU(x) Hw = torch.tanh(self.w_proj(x)) w_score = self.softmax(Hw.matmul(self.w_context_vector)) x = x.mul(w_score) # print(x.size()) return x ================================================ FILE: code/Attentive_BiLSTM/Word_Freqency_Mapper.py ================================================ from gaussian_binner import GaussianBinner import numpy as np from collections import Counter class Word_Freqency_Mapper: """docstring for ClassName""" def __init__(self,bins=100, w=5.0): self.Train_Word_Counter=Counter() self.set_of_freq=set() self.binner = GaussianBinner(bins=bins, w=w) self.all_word_w_freq_vector={} self.Test_Set_Words=set() def Find_Freq_Vector_for_words(self): ip_array=np.array([0]) for word in self.Train_Word_Counter: word_freq=np.array([self.Train_Word_Counter[word]]) temp_ip_array = np.vstack((ip_array,word_freq)) freq_vector= self.binner.transform(temp_ip_array,1)[1] self.all_word_w_freq_vector[word]=freq_vector # print(freq_vector.shape) # print(word) # print(freq_vector[1]) # print(self.Test_Set_Words - (set())) # print(len(self.Train_Word_Counter.keys())) for word in self.Test_Set_Words: if word in self.Train_Word_Counter: # continue # word_freq1= self.all_word_w_freq_vector[word] word_freq=np.array([self.Train_Word_Counter[word]]) # print(np.subtract(word_freq1, word_freq1)) # print("\n\n\n") continue else: word_freq=np.array([0]) # print(word, word_freq) temp_ip_array = np.vstack((ip_array,word_freq)) freq_vector= self.binner.transform(temp_ip_array,1)[1] # print(word, freq_vector) self.all_word_w_freq_vector[word]=freq_vector # print(self.all_word_w_freq_vector) def Read_File(self, ip_file): list_of_sentence_words_in_file=[] list_of_sentence_labels_in_file=[] list_of_markdown_markdowns_in_file=[] current_sent_words=[] current_sent_labels=[] current_sent_markdowns=[] for line in open(ip_file): #print(line) if line.strip()=="": if len(current_sent_words)>0: output_line = " ".join(current_sent_words) #print(output_line) if "code omitted for annotation" in output_line and "CODE_BLOCK :" in output_line: current_sent_words=[] current_sent_labels=[] current_sent_markdowns=[] continue elif "omitted for annotation" in output_line and "OP_BLOCK :" in output_line: current_sent_words=[] current_sent_labels=[] current_sent_markdowns=[] continue elif "Question_URL :" in output_line: current_sent_words=[] current_sent_labels=[] current_sent_markdowns=[] continue elif "Question_ID :" in output_line: current_sent_words=[] current_sent_labels=[] current_sent_markdowns=[] continue else: list_of_sentence_words_in_file.append(current_sent_words) list_of_sentence_labels_in_file.append(current_sent_labels) list_of_markdown_markdowns_in_file.append(current_sent_markdowns) current_sent_words=[] current_sent_labels=[] current_sent_markdowns=[] else: line_values=line.strip().split() gold_word=line_values[0] gold_label=line_values[1] raw_word=line_values[2] raw_label=line_values[3] word=gold_word label=gold_label current_sent_words.append(word) current_sent_markdowns.append(raw_label) current_sent_labels.append(label) return (list_of_sentence_words_in_file, list_of_sentence_labels_in_file, list_of_markdown_markdowns_in_file) def Read_Test_Data(self, input_test_file): (list_of_sentence_words_in_file, list_of_sentence_labels_in_file, list_of_markdown_markdowns_in_file) = self.Read_File(input_test_file) for list_of_words in list_of_sentence_words_in_file: for word in list_of_words: self.Test_Set_Words.add(word) def Read_Dev_Data(self, input_dev_file): (list_of_sentence_words_in_file, list_of_sentence_labels_in_file, list_of_markdown_markdowns_in_file) = self.Read_File(input_dev_file) for list_of_words in list_of_sentence_words_in_file: for word in list_of_words: self.Test_Set_Words.add(word) def Find_Train_Data_Freq(self, input_train_file): (list_of_sentence_words_in_file, list_of_sentence_labels_in_file, list_of_markdown_markdowns_in_file) = self.Read_File(input_train_file) for list_of_words in list_of_sentence_words_in_file: for word in list_of_words: self.Train_Word_Counter[word]+=1 for word in self.Train_Word_Counter: self.set_of_freq.add(self.Train_Word_Counter[word]) # print(min(self.set_of_freq)) def Find_Gaussian_Bining_For_Training_Data_Freq(self): freq_array=np.array([0]) for word in self.Train_Word_Counter: word_freq =np.array([self.Train_Word_Counter[word]]) freq_array = np.vstack((freq_array,word_freq )) self.binner.fit(freq_array, 1) def Write_Freq_To_File(self,output_file): fout= open(output_file,'w') for word in self.all_word_w_freq_vector: word_freq=self.all_word_w_freq_vector[word].tolist() word_freq_str=[str(x) for x in word_freq] # print(word_freq) # word_freq_str = np.array2string(word_freq, formatter={'str':lambda x: float(word_freq)}) # print(word_freq_str) # print("\n\n\n") opline=word+" "+" ".join(word_freq_str)+"\n" # print(opline) fout.write(opline) fout.close() if __name__ == '__main__': input_train_file="../../StackOverflow_Input_Data/train_gold_raw_merged.txt" input_dev_file="../../StackOverflow_Input_Data/dev_gold_raw_merged.txt" input_test_file="../../StackOverflow_Input_Data/test_gold_raw_merged.txt" output_file="Freq_Vector.txt" freq_mapper = Word_Freqency_Mapper() freq_mapper.Find_Train_Data_Freq(input_train_file) freq_mapper.Read_Dev_Data(input_dev_file) freq_mapper.Read_Test_Data(input_test_file) freq_mapper.Find_Gaussian_Bining_For_Training_Data_Freq() freq_mapper.Find_Freq_Vector_for_words() freq_mapper.Write_Freq_To_File(output_file) ================================================ FILE: 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