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Repository: brucewlee/lingfeat
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Total size: 118.1 MB
Directory structure:
gitextract_0oqnzvih/
├── .gitattributes
├── .gitignore
├── LICENSE.txt
├── README.md
├── lingfeat/
│ ├── _AdvancedSemantic/
│ │ ├── OSKF.py
│ │ ├── WBKF.py
│ │ ├── WoKF.py
│ │ ├── __init__.py
│ │ └── model/
│ │ ├── __init__.py
│ │ ├── enwiki100
│ │ ├── enwiki100.expElogbeta.npy
│ │ ├── enwiki100.state
│ │ ├── enwiki150
│ │ ├── enwiki150.expElogbeta.npy
│ │ ├── enwiki150.state
│ │ ├── enwiki150.state.sstats.npy
│ │ ├── enwiki200
│ │ ├── enwiki200.expElogbeta.npy
│ │ ├── enwiki200.state
│ │ ├── enwiki200.state.sstats.npy
│ │ ├── enwiki50
│ │ ├── enwiki50.expElogbeta.npy
│ │ ├── enwiki50.state
│ │ ├── onestop100
│ │ ├── onestop100.expElogbeta.npy
│ │ ├── onestop100.state
│ │ ├── onestop150
│ │ ├── onestop150.expElogbeta.npy
│ │ ├── onestop150.state
│ │ ├── onestop200
│ │ ├── onestop200.expElogbeta.npy
│ │ ├── onestop200.state
│ │ ├── onestop50
│ │ ├── onestop50.expElogbeta.npy
│ │ ├── onestop50.state
│ │ ├── weebit100
│ │ ├── weebit100.expElogbeta.npy
│ │ ├── weebit100.state
│ │ ├── weebit150
│ │ ├── weebit150.expElogbeta.npy
│ │ ├── weebit150.state
│ │ ├── weebit200
│ │ ├── weebit200.expElogbeta.npy
│ │ ├── weebit200.state
│ │ ├── weebit50
│ │ ├── weebit50.expElogbeta.npy
│ │ └── weebit50.state
│ ├── _Discourse/
│ │ ├── EnDF.py
│ │ ├── EnGF.py
│ │ └── __init__.py
│ ├── _LexicoSemantic/
│ │ ├── PsyF.py
│ │ ├── TTRF.py
│ │ ├── VarF.py
│ │ ├── WorF.py
│ │ ├── __init__.py
│ │ └── resources/
│ │ ├── AoAKuperman.csv
│ │ └── SUBTLEXus.csv
│ ├── _ShallowTraditional/
│ │ ├── ShaF.py
│ │ ├── TraF.py
│ │ └── __init__.py
│ ├── _Syntactic/
│ │ ├── POSF.py
│ │ ├── PhrF.py
│ │ ├── TrSF.py
│ │ └── __init__.py
│ ├── __init__.py
│ ├── extractor.py
│ └── utils.py
├── requirements.txt
├── setup.cfg
├── setup.py
└── test-lingfeat.py
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FILE: README.md
================================================
[![CC BY-SA 4.0][cc-by-sa-shield]][cc-by-sa]
[](https://spacy.io)
<a href="https://pypi.org/project/LingFeat"><img alt="PyPI" src="https://img.shields.io/badge/pypi-supported-yellow"></a>
<img alt="Python 3.5+" src="https://img.shields.io/badge/python-3.5%2B-yellowgreen"></a>
<img alt="LingFeat" src="https://img.shields.io/badge/LingFeat-v.1.0.0--beta.19-red"></a>
<p align="center"><img src="img/lingfeat_logo.png" width="500" /> </p>
# LingFeat - Comprehensive Linguistic Features Extraction Tool for Readability Assessment and Text Simplification
- [LingFeat - Comprehensive Linguistic Features Extraction Tool for Readability Assessment and Text Simplification](#lingfeat---comprehensive-linguistic-features-extraction-tool-for-readability-assessment-and-text-simplification)
- [Overview](#overview)
- [Things to note](#things-to-note)
- [Citation](#citation)
- [Installation](#installation)
- [Usage](#usage)
- [Available Features, Code, Definition](#available-features-code-definition)
- [Key References](#key-references)
- [License](#license)
- [Acknowledgement](#acknowledgement)
## Migration Notice - 2023-03-06
LingFeat is now maintained in a new repository named [LFTK](https://github.com/brucewlee/lftk). The new library will have more focus on usability, coverage, multilingualism, and expandability.
## Upgrade Notice - 2022-10-18
I am currently updating this repository, including project structure, feature coverage, and etc. The already existing issues will be reflected, too. Please email at brucelws@seas.upenn.edu for any suggestions. Thank you community for the patience.
## Overview
LingFeat is a Python research package for various handcrafted linguistic features. More specifically, LingFeat is an NLP feature extraction software, which currently extracts 255 linguistic features from **English** string input.
These features can be divided into five broad linguistic branches:
1. **Advanced Semantic** (AdSem): for measuing complexity of meaning structures (**Not working in some cases. Working on this issue.**)
- *Semantic Richness, Noise, and Clarity from trained LDA models (included, no training required)*
2. **Discourse** (Disco): for measuring coherence/cohesion
- *Entity Counts, Entity Grid, and Local Coherence score*
3. **Syntactic** (Synta): for measuring the complexity of grammar and structure
- *Phrasal Counts (e.g. Noun Phrase), Part-of-Speech Counts, and Tree Structure*
4. **Lexico Semantic** (LxSem): for measuring word/phrasal-specific difficulty
- *Type Token Ratio, Variation Score (e.g. Verb Variation), Age-of-Acquistion, and SubtlexUS Frequency*
5. **Shallow Traditional** (ShTra): traditional features/formulas for text difficulty
- *Basic Average Counts (words per sentence), Flesch-Kincaid Reading Ease, Smog, Gunning Fog, ...*
## Things to note
LingFeat is mainly built for text complexity/difficulty/readability analysis or text simplification studies. But it's role is to simply extract numerical linguistic faetures from a text. Hence, the use cases may vary.
We provide guidelines for both basic users and advanced users. Please follow [Usage](#usage) section.
## Citation
This software is built for our paper on
> @inproceedings{lee-etal-2021-pushing,\
> title = "Pushing on Text Readability Assessment: A Transformer Meets Handcrafted Linguistic Features",\
> author = "Lee, Bruce W. and Jang, Yoo Sung and Lee, Jason",\
> booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",\
> month = nov,\
> year = "2021",\
> address = "Online and Punta Cana, Dominican Republic",\
> publisher = "Association for Computational Linguistics",\
> url = "https://aclanthology.org/2021.emnlp-main.834"
> doi = "10.18653/v1/2021.emnlp-main.834",\
> pages = "10669--10686"}
*Please cite our paper and provide link to this repository* if you use in this software in research.
Most supported features are developed for passage analysis. One sentence input will work fine with the program but won't produce reliable output.
## Installation
**Option 1.** Use package manager [pip](https://pip.pypa.io/en/stable/) to install LingFeat.
```bash
pip install lingfeat
```
**Option 2.** Install from the repo. (Recommended)
You'll need to install the dependencies, including spaCy, by yourself. Ideally, use virtual environment (optional).
Use code below for option 2.
```bash
git clone https://github.com/brucewlee/lingfeat.git
pip install -r lingfeat/requirements.txt
```
## Usage
A. **General Purpose (basic)**
If you aren't deeply interested in linguistics, you usually don't require the full feature set of LingFeat.
The following code returns a dictionary of 6 outputs from commonly used formulas in predicting readability:
- Flesch Kincaid Grade Level *(Feature Code: FleschG_S)*
- Automated Readability Index *(Feature Code: AutoRea_S)*
- Coleman Liau Readability Score *(Feature Code: ColeLia_S)*
- Smog Index *(Feature Code: SmogInd_S)*
- Gunning Fog Count Score *(Feature Code: Gunning_S)*
- Linsear Write Formula Score *(Feature Code: LinseaW_S)*
These formulas are a little outdated... but still widely used.
They are designed to match U.S. grade level from 1~12 (i.e. average student of the grade can read the text).
Ideally, you could average these 6 outputs to obtain a reliable outcome.
```python
from lingfeat import extractor
text = "..."
LingFeat = extractor.pass_text(text)
LingFeat.preprocess()
TraF = LingFeat.TraF_()
print(TraF)
```
B. **Research/ML/NLP Purpose (advanced)**
B.1 ***Spacy Requirements***
This library assumes that you have spaCy sm corpus (that is compatible with spaCy 3.0+) installed. If not, or if you aren't sure, run the following in terminal.
```bash
python -m spacy download en_core_web_sm
```
B.2. ***Example***
Due to the wide number of supported features, we defined *subgroups* for features. Hence, features are not accessible individually. Instead, you'd call the *subgroups* to obtain the dictionary of the corresponding features.
To broadly understand how these features interact with text readability, difficulty, and complexity, I highly suggest you read Section 2 and 3 in our EMNLP paper.
```python
"""
Import
this is the only import you need
"""
from lingfeat import extractor
"""
Pass text
here, text must be in string type
"""
text = "..."
LingFeat = extractor.pass_text(text)
"""
Preprocess text
options (all boolean):
- short (default False): include short words of < 3 letters
- see_token (default False): return token list
- see_sent_token (default False): return tokens in sentences
output:
- n_token
- n_sent
- token_list (optional)
- sent_token_list (optional)
"""
LingFeat.preprocess()
# or
# print(LingFeat.preprocess())
"""
Extract features
each method returns a dictionary of the corresponding features
"""
# Advanced Semantic (AdSem) Features
WoKF = LingFeat.WoKF_() # Wikipedia Knowledge Features
WBKF = LingFeat.WBKF_() # WeeBit Corpus Knowledge Features
OSKF = LingFeat.OSKF_() # OneStopEng Corpus Knowledge Features
# Discourse (Disco) Features
EnDF = LingFeat.EnDF_() # Entity Density Features
EnGF = LingFeat.EnGF_() # Entity Grid Features
# Syntactic (Synta) Features
PhrF = LingFeat.PhrF_() # Noun/Verb/Adj/Adv/... Phrasal Features
TrSF = LingFeat.TrSF_() # (Parse) Tree Structural Features
POSF = LingFeat.POSF_() # Noun/Verb/Adj/Adv/... Part-of-Speech Features
# Lexico Semantic (LxSem) Features
TTRF = LingFeat.TTRF_() # Type Token Ratio Features
VarF = LingFeat.VarF_() # Noun/Verb/Adj/Adv Variation Features
PsyF = LingFeat.PsyF_() # Psycholinguistic Difficulty of Words (AoA Kuperman)
WoLF = LingFeat.WorF_() # Word Familiarity from Frequency Count (SubtlexUS)
# Shallow Traditional (ShTra) Features
ShaF = LingFeat.ShaF_() # Shallow Features (e.g. avg number of tokens)
TraF = LingFeat.TraF_() # Traditional Formulas
```
## Available Features, Code, Definition
| idx | Linguistic Branch | Subgroup Code | Subgroup Definition | Feature Code | Feature Definition |
|-------|---------------------|---------------|--------------------------------------|--------------|--------------------------------------------------------------------------------|
| 1 | AdSem | WoKF_ | Wiki Knowledge Features | WRich05_S | Semantic Richness, 50 topics extracted from Wikipedia |
| 2 | AdSem | WoKF_ | Wiki Knowledge Features | WClar05_S | Semantic Clarity, 50 topics extracted from Wikipedia |
| 3 | AdSem | WoKF_ | Wiki Knowledge Features | WNois05_S | Semantic Noise, 50 topics extracted from Wikipedia |
| 4 | AdSem | WoKF_ | Wiki Knowledge Features | WTopc05_S | Number of topics, 50 topics extracted from Wikipedia |
| 5 | AdSem | WoKF_ | Wiki Knowledge Features | WRich10_S | Semantic Richness, 100 topics extracted from Wikipedia |
| 6 | AdSem | WoKF_ | Wiki Knowledge Features | WClar10_S | Semantic Clarity, 100 topics extracted from Wikipedia |
| 7 | AdSem | WoKF_ | Wiki Knowledge Features | WNois10_S | Semantic Noise, 100 topics extracted from Wikipedia |
| 8 | AdSem | WoKF_ | Wiki Knowledge Features | WTopc10_S | Number of topics, 100 topics extracted from Wikipedia |
| 9 | AdSem | WoKF_ | Wiki Knowledge Features | WRich15_S | Semantic Richness, 150 topics extracted from Wikipedia |
| 10 | AdSem | WoKF_ | Wiki Knowledge Features | WClar15_S | Semantic Clarity, 150 topics extracted from Wikipedia |
| 11 | AdSem | WoKF_ | Wiki Knowledge Features | WNois15_S | Semantic Noise, 150 topics extracted from Wikipedia |
| 12 | AdSem | WoKF_ | Wiki Knowledge Features | WTopc15_S | Number of topics, 150 topics extracted from Wikipedia |
| 13 | AdSem | WoKF_ | Wiki Knowledge Features | WRich20_S | Semantic Richness, 200 topics extracted from Wikipedia |
| 14 | AdSem | WoKF_ | Wiki Knowledge Features | WClar20_S | Semantic Clarity, 200 topics extracted from Wikipedia |
| 15 | AdSem | WoKF_ | Wiki Knowledge Features | WNois20_S | Semantic Noise, 200 topics extracted from Wikipedia |
| 16 | AdSem | WoKF_ | Wiki Knowledge Features | WTopc20_S | Number of topics, 200 topics extracted from Wikipedia |
| 17 | AdSem | WBKF_ | WB Knowledge Features | BRich05_S | Semantic Richness, 50 topics extracted from WeeBit Corpus |
| 18 | AdSem | WBKF_ | WB Knowledge Features | BClar05_S | Semantic Clarity, 50 topics extracted from WeeBit Corpus |
| 19 | AdSem | WBKF_ | WB Knowledge Features | BNois05_S | Semantic Noise, 50 topics extracted from WeeBit Corpus |
| 20 | AdSem | WBKF_ | WB Knowledge Features | BTopc05_S | Number of topics, 50 topics extracted from WeeBit Corpus |
| 21 | AdSem | WBKF_ | WB Knowledge Features | BRich10_S | Semantic Richness, 100 topics extracted from WeeBit Corpus |
| 22 | AdSem | WBKF_ | WB Knowledge Features | BClar10_S | Semantic Clarity, 100 topics extracted from WeeBit Corpus |
| 23 | AdSem | WBKF_ | WB Knowledge Features | BNois10_S | Semantic Noise, 100 topics extracted from WeeBit Corpus |
| 24 | AdSem | WBKF_ | WB Knowledge Features | BTopc10_S | Number of topics, 100 topics extracted from WeeBit Corpus |
| 25 | AdSem | WBKF_ | WB Knowledge Features | BRich15_S | Semantic Richness, 150 topics extracted from WeeBit Corpus |
| 26 | AdSem | WBKF_ | WB Knowledge Features | BClar15_S | Semantic Clarity, 150 topics extracted from WeeBit Corpus |
| 27 | AdSem | WBKF_ | WB Knowledge Features | BNois15_S | Semantic Noise, 150 topics extracted from WeeBit Corpus |
| 28 | AdSem | WBKF_ | WB Knowledge Features | BTopc15_S | Number of topics, 150 topics extracted from WeeBit Corpus |
| 29 | AdSem | WBKF_ | WB Knowledge Features | BRich20_S | Semantic Richness, 200 topics extracted from WeeBit Corpus |
| 30 | AdSem | WBKF_ | WB Knowledge Features | BClar20_S | Semantic Clarity, 200 topics extracted from WeeBit Corpus |
| 31 | AdSem | WBKF_ | WB Knowledge Features | BNois20_S | Semantic Noise, 200 topics extracted from WeeBit Corpus |
| 32 | AdSem | WBKF_ | WB Knowledge Features | BTopc20_S | Number of topics, 200 topics extracted from WeeBit Corpus |
| 33 | AdSem | OSKF_ | OSE Knowledge Features | ORich05_S | Semantic Richness, 50 topics extracted from OneStopEng Corpus |
| 34 | AdSem | OSKF_ | OSE Knowledge Features | OClar05_S | Semantic Clarity, 50 topics extracted from OneStopEng Corpus |
| 35 | AdSem | OSKF_ | OSE Knowledge Features | ONois05_S | Semantic Noise, 50 topics extracted from OneStopEng Corpus |
| 36 | AdSem | OSKF_ | OSE Knowledge Features | OTopc05_S | Number of topics, 50 topics extracted from OneStopEng Corpus |
| 37 | AdSem | OSKF_ | OSE Knowledge Features | ORich10_S | Semantic Richness, 100 topics extracted from OneStopEng Corpus |
| 38 | AdSem | OSKF_ | OSE Knowledge Features | OClar10_S | Semantic Clarity, 100 topics extracted from OneStopEng Corpus |
| 39 | AdSem | OSKF_ | OSE Knowledge Features | ONois10_S | Semantic Noise, 100 topics extracted from OneStopEng Corpus |
| 40 | AdSem | OSKF_ | OSE Knowledge Features | OTopc10_S | Number of topics, 100 topics extracted from OneStopEng Corpus |
| 41 | AdSem | OSKF_ | OSE Knowledge Features | ORich15_S | Semantic Richness, 150 topics extracted from OneStopEng Corpus |
| 42 | AdSem | OSKF_ | OSE Knowledge Features | OClar15_S | Semantic Clarity, 150 topics extracted from OneStopEng Corpus |
| 43 | AdSem | OSKF_ | OSE Knowledge Features | ONois15_S | Semantic Noise, 150 topics extracted from OneStopEng Corpus |
| 44 | AdSem | OSKF_ | OSE Knowledge Features | OTopc15_S | Number of topics, 150 topics extracted from OneStopEng Corpus |
| 45 | AdSem | OSKF_ | OSE Knowledge Features | ORich20_S | Semantic Richness, 200 topics extracted from OneStopEng Corpus |
| 46 | AdSem | OSKF_ | OSE Knowledge Features | OClar20_S | Semantic Clarity, 200 topics extracted from OneStopEng Corpus |
| 47 | AdSem | OSKF_ | OSE Knowledge Features | ONois20_S | Semantic Noise, 200 topics extracted from OneStopEng Corpus |
| 48 | AdSem | OSKF_ | OSE Knowledge Features | OTopc20_S | Number of topics, 200 topics extracted from OneStopEng Corpus |
| 49 | Disco | EnDF_ | Entity Density Features | to_EntiM_C | total number of Entities Mentions counts |
| 50 | Disco | EnDF_ | Entity Density Features | as_EntiM_C | average number of Entities Mentions counts per sentence |
| 51 | Disco | EnDF_ | Entity Density Features | at_EntiM_C | average number of Entities Mentions counts per token (word) |
| 52 | Disco | EnDF_ | Entity Density Features | to_UEnti_C | total number of unique Entities |
| 53 | Disco | EnDF_ | Entity Density Features | as_UEnti_C | average number of unique Entities per sentence |
| 54 | Disco | EnDF_ | Entity Density Features | at_UEnti_C | average number of unique Entities per token (word) |
| 55 | Disco | EnGF_ | Entity Grid Features | ra_SSToT_C | ratio of ss transitions to total |
| 56 | Disco | EnGF_ | Entity Grid Features | ra_SOToT_C | ratio of so transitions to total |
| 57 | Disco | EnGF_ | Entity Grid Features | ra_SXToT_C | ratio of sx transitions to total |
| 58 | Disco | EnGF_ | Entity Grid Features | ra_SNToT_C | ratio of sn transitions to total |
| 59 | Disco | EnGF_ | Entity Grid Features | ra_OSToT_C | ratio of os transitions to total |
| 60 | Disco | EnGF_ | Entity Grid Features | ra_OOToT_C | ratio of oo transitions to total |
| 61 | Disco | EnGF_ | Entity Grid Features | ra_OXToT_C | ratio of ox transitions to total |
| 62 | Disco | EnGF_ | Entity Grid Features | ra_ONToT_C | ratio of on transitions to total |
| 63 | Disco | EnGF_ | Entity Grid Features | ra_XSToT_C | ratio of xs transitions to total |
| 64 | Disco | EnGF_ | Entity Grid Features | ra_XOToT_C | ratio of xo transitions to total |
| 65 | Disco | EnGF_ | Entity Grid Features | ra_XXToT_C | ratio of xx transitions to total |
| 66 | Disco | EnGF_ | Entity Grid Features | ra_XNToT_C | ratio of xn transitions to total |
| 67 | Disco | EnGF_ | Entity Grid Features | ra_NSToT_C | ratio of ns transitions to total |
| 68 | Disco | EnGF_ | Entity Grid Features | ra_NOToT_C | ratio of no transitions to total |
| 69 | Disco | EnGF_ | Entity Grid Features | ra_NXToT_C | ratio of nx transitions to total |
| 70 | Disco | EnGF_ | Entity Grid Features | ra_NNToT_C | ratio of nn transitions to total |
| 71 | Disco | EnGF_ | Entity Grid Features | LoCohPA_S | Local Coherence for PA score |
| 72 | Disco | EnGF_ | Entity Grid Features | LoCohPW_S | Local Coherence for PW score |
| 73 | Disco | EnGF_ | Entity Grid Features | LoCohPU_S | Local Coherence for PU score |
| 74 | Disco | EnGF_ | Entity Grid Features | LoCoDPA_S | Local Coherence distance for PA score |
| 75 | Disco | EnGF_ | Entity Grid Features | LoCoDPW_S | Local Coherence distance for PW score |
| 76 | Disco | EnGF_ | Entity Grid Features | LoCoDPU_S | Local Coherence distance for PU score |
| 77 | Synta | PhrF_ | Phrasal Features | to_NoPhr_C | total count of Noun phrases |
| 78 | Synta | PhrF_ | Phrasal Features | as_NoPhr_C | average count of Noun phrases per sentence |
| 79 | Synta | PhrF_ | Phrasal Features | at_NoPhr_C | average count of Noun phrases per token |
| 80 | Synta | PhrF_ | Phrasal Features | ra_NoVeP_C | ratio of Noun phrases count to Verb phrases count |
| 81 | Synta | PhrF_ | Phrasal Features | ra_NoSuP_C | ratio of Noun phrases count to Subordinate Clauses count |
| 82 | Synta | PhrF_ | Phrasal Features | ra_NoPrP_C | ratio of Noun phrases count to Prep phrases count |
| 83 | Synta | PhrF_ | Phrasal Features | ra_NoAjP_C | ratio of Noun phrases count to Adj phrases count |
| 84 | Synta | PhrF_ | Phrasal Features | ra_NoAvP_C | ratio of Noun phrases count to Adv phrases count |
| 85 | Synta | PhrF_ | Phrasal Features | to_VePhr_C | total count of Verb phrases |
| 86 | Synta | PhrF_ | Phrasal Features | as_VePhr_C | average count of Verb phrases per sentence |
| 87 | Synta | PhrF_ | Phrasal Features | at_VePhr_C | average count of Verb phrases per token |
| 88 | Synta | PhrF_ | Phrasal Features | ra_VeNoP_C | ratio of Verb phrases count to Noun phrases count |
| 89 | Synta | PhrF_ | Phrasal Features | ra_VeSuP_C | ratio of Verb phrases count to Subordinate Clauses count |
| 90 | Synta | PhrF_ | Phrasal Features | ra_VePrP_C | ratio of Verb phrases count to Prep phrases count |
| 91 | Synta | PhrF_ | Phrasal Features | ra_VeAjP_C | ratio of Verb phrases count to Adj phrases count |
| 92 | Synta | PhrF_ | Phrasal Features | ra_VeAvP_C | ratio of Verb phrases count to Adv phrases count |
| 93 | Synta | PhrF_ | Phrasal Features | to_SuPhr_C | total count of Subordinate Clauses |
| 94 | Synta | PhrF_ | Phrasal Features | as_SuPhr_C | average count of Subordinate Clauses per sentence |
| 95 | Synta | PhrF_ | Phrasal Features | at_SuPhr_C | average count of Subordinate Clauses per token |
| 96 | Synta | PhrF_ | Phrasal Features | ra_SuNoP_C | ratio of Subordinate Clauses count to Noun phrases count |
| 97 | Synta | PhrF_ | Phrasal Features | ra_SuVeP_C | ratio of Subordinate Clauses count to Verb phrases count |
| 98 | Synta | PhrF_ | Phrasal Features | ra_SuPrP_C | ratio of Subordinate Clauses count to Prep phrases count |
| 99 | Synta | PhrF_ | Phrasal Features | ra_SuAjP_C | ratio of Subordinate Clauses count to Adj phrases count |
| 100 | Synta | PhrF_ | Phrasal Features | ra_SuAvP_C | ratio of Subordinate Clauses count to Adv phrases count |
| 101 | Synta | PhrF_ | Phrasal Features | to_PrPhr_C | total count of prepositional phrases |
| 102 | Synta | PhrF_ | Phrasal Features | as_PrPhr_C | average count of prepositional phrases per sentence |
| 103 | Synta | PhrF_ | Phrasal Features | at_PrPhr_C | average count of prepositional phrases per token |
| 104 | Synta | PhrF_ | Phrasal Features | ra_PrNoP_C | ratio of Prep phrases count to Noun phrases count |
| 105 | Synta | PhrF_ | Phrasal Features | ra_PrVeP_C | ratio of Prep phrases count to Verb phrases count |
| 106 | Synta | PhrF_ | Phrasal Features | ra_PrSuP_C | ratio of Prep phrases count to Subordinate Clauses count |
| 107 | Synta | PhrF_ | Phrasal Features | ra_PrAjP_C | ratio of Prep phrases count to Adj phrases count |
| 108 | Synta | PhrF_ | Phrasal Features | ra_PrAvP_C | ratio of Prep phrases count to Adv phrases count |
| 109 | Synta | PhrF_ | Phrasal Features | to_AjPhr_C | total count of Adjective phrases |
| 110 | Synta | PhrF_ | Phrasal Features | as_AjPhr_C | average count of Adjective phrases per sentence |
| 111 | Synta | PhrF_ | Phrasal Features | at_AjPhr_C | average count of Adjective phrases per token |
| 112 | Synta | PhrF_ | Phrasal Features | ra_AjNoP_C | ratio of Adj phrases count to Noun phrases count |
| 113 | Synta | PhrF_ | Phrasal Features | ra_AjVeP_C | ratio of Adj phrases count to Verb phrases count |
| 114 | Synta | PhrF_ | Phrasal Features | ra_AjSuP_C | ratio of Adj phrases count to Subordinate Clauses count |
| 115 | Synta | PhrF_ | Phrasal Features | ra_AjPrP_C | ratio of Adj phrases count to Prep phrases count |
| 116 | Synta | PhrF_ | Phrasal Features | ra_AjAvP_C | ratio of Adj phrases count to Adv phrases count |
| 117 | Synta | PhrF_ | Phrasal Features | to_AvPhr_C | total count of Adverb phrases |
| 118 | Synta | PhrF_ | Phrasal Features | as_AvPhr_C | average count of Adverb phrases per sentence |
| 119 | Synta | PhrF_ | Phrasal Features | at_AvPhr_C | average count of Adverb phrases per token |
| 120 | Synta | PhrF_ | Phrasal Features | ra_AvNoP_C | ratio of Adv phrases count to Noun phrases count |
| 121 | Synta | PhrF_ | Phrasal Features | ra_AvVeP_C | ratio of Adv phrases count to Verb phrases count |
| 122 | Synta | PhrF_ | Phrasal Features | ra_AvSuP_C | ratio of Adv phrases count to Subordinate Clauses count |
| 123 | Synta | PhrF_ | Phrasal Features | ra_AvPrP_C | ratio of Adv phrases count to Prep phrases count |
| 124 | Synta | PhrF_ | Phrasal Features | ra_AvAjP_C | ratio of Adv phrases count to Adj phrases count |
| 125 | Synta | TrSF_ | Tree Structure Features | to_TreeH_C | total Tree height of all sentences |
| 126 | Synta | TrSF_ | Tree Structure Features | as_TreeH_C | average Tree height per sentence |
| 127 | Synta | TrSF_ | Tree Structure Features | at_TreeH_C | average Tree height per token (word) |
| 128 | Synta | TrSF_ | Tree Structure Features | to_FTree_C | total length of flattened Trees |
| 129 | Synta | TrSF_ | Tree Structure Features | as_FTree_C | average length of flattened Trees per sentence |
| 130 | Synta | TrSF_ | Tree Structure Features | at_FTree_C | average length of flattened Trees per token (word) |
| 131 | Synta | POSF_ | Part-of-Speech Features | to_NoTag_C | total count of Noun POS tags |
| 132 | Synta | POSF_ | Part-of-Speech Features | as_NoTag_C | average count of Noun POS tags per sentence |
| 133 | Synta | POSF_ | Part-of-Speech Features | at_NoTag_C | average count of Noun POS tags per token |
| 134 | Synta | POSF_ | Part-of-Speech Features | ra_NoAjT_C | ratio of Noun POS count to Adjective POS count |
| 135 | Synta | POSF_ | Part-of-Speech Features | ra_NoVeT_C | ratio of Noun POS count to Verb POS count |
| 136 | Synta | POSF_ | Part-of-Speech Features | ra_NoAvT_C | ratio of Noun POS count to Adverb POS count |
| 137 | Synta | POSF_ | Part-of-Speech Features | ra_NoSuT_C | ratio of Noun POS count to Subordinating Conjunction count |
| 138 | Synta | POSF_ | Part-of-Speech Features | ra_NoCoT_C | ratio of Noun POS count to Coordinating Conjunction count |
| 139 | Synta | POSF_ | Part-of-Speech Features | to_VeTag_C | total count of Verb POS tags |
| 140 | Synta | POSF_ | Part-of-Speech Features | as_VeTag_C | average count of Verb POS tags per sentence |
| 141 | Synta | POSF_ | Part-of-Speech Features | at_VeTag_C | average count of Verb POS tags per token |
| 142 | Synta | POSF_ | Part-of-Speech Features | ra_VeAjT_C | ratio of Verb POS count to Adjective POS count |
| 143 | Synta | POSF_ | Part-of-Speech Features | ra_VeNoT_C | ratio of Verb POS count to Noun POS count |
| 144 | Synta | POSF_ | Part-of-Speech Features | ra_VeAvT_C | ratio of Verb POS count to Adverb POS count |
| 145 | Synta | POSF_ | Part-of-Speech Features | ra_VeSuT_C | ratio of Verb POS count to Subordinating Conjunction count |
| 146 | Synta | POSF_ | Part-of-Speech Features | ra_VeCoT_C | ratio of Verb POS count to Coordinating Conjunction count |
| 147 | Synta | POSF_ | Part-of-Speech Features | to_AjTag_C | total count of Adjective POS tags |
| 148 | Synta | POSF_ | Part-of-Speech Features | as_AjTag_C | average count of Adjective POS tags per sentence |
| 149 | Synta | POSF_ | Part-of-Speech Features | at_AjTag_C | average count of Adjective POS tags per token |
| 150 | Synta | POSF_ | Part-of-Speech Features | ra_AjNoT_C | ratio of Adjective POS count to Noun POS count |
| 151 | Synta | POSF_ | Part-of-Speech Features | ra_AjVeT_C | ratio of Adjective POS count to Verb POS count |
| 152 | Synta | POSF_ | Part-of-Speech Features | ra_AjAvT_C | ratio of Adjective POS count to Adverb POS count |
| 153 | Synta | POSF_ | Part-of-Speech Features | ra_AjSuT_C | ratio of Adjective POS count to Subordinating Conjunction count |
| 154 | Synta | POSF_ | Part-of-Speech Features | ra_AjCoT_C | ratio of Adjective POS count to Coordinating Conjunction count |
| 155 | Synta | POSF_ | Part-of-Speech Features | to_AvTag_C | total count of Adverb POS tags |
| 156 | Synta | POSF_ | Part-of-Speech Features | as_AvTag_C | average count of Adverb POS tags per sentence |
| 157 | Synta | POSF_ | Part-of-Speech Features | at_AvTag_C | average count of Adverb POS tags per token |
| 158 | Synta | POSF_ | Part-of-Speech Features | ra_AvAjT_C | ratio of Adverb POS count to Adjective POS count |
| 159 | Synta | POSF_ | Part-of-Speech Features | ra_AvNoT_C | ratio of Adverb POS count to Noun POS count |
| 160 | Synta | POSF_ | Part-of-Speech Features | ra_AvVeT_C | ratio of Adverb POS count to Verb POS count |
| 161 | Synta | POSF_ | Part-of-Speech Features | ra_AvSuT_C | ratio of Adverb POS count to Subordinating Conjunction count |
| 162 | Synta | POSF_ | Part-of-Speech Features | ra_AvCoT_C | ratio of Adverb POS count to Coordinating Conjunction count |
| 163 | Synta | POSF_ | Part-of-Speech Features | to_SuTag_C | total count of Subordinating Conjunction POS tags |
| 164 | Synta | POSF_ | Part-of-Speech Features | as_SuTag_C | average count of Subordinating Conjunction POS tags per sentence |
| 165 | Synta | POSF_ | Part-of-Speech Features | at_SuTag_C | average count of Subordinating Conjunction POS tags per token |
| 166 | Synta | POSF_ | Part-of-Speech Features | ra_SuAjT_C | ratio of Subordinating Conjunction POS count to Adjective POS count |
| 167 | Synta | POSF_ | Part-of-Speech Features | ra_SuNoT_C | ratio of Subordinating Conjunction POS count to Noun POS count |
| 168 | Synta | POSF_ | Part-of-Speech Features | ra_SuVeT_C | ratio of Subordinating Conjunction POS count to Verb POS count |
| 169 | Synta | POSF_ | Part-of-Speech Features | ra_SuAvT_C | ratio of Subordinating Conjunction POS count to Adverb POS count |
| 170 | Synta | POSF_ | Part-of-Speech Features | ra_SuCoT_C | ratio of Subordinating Conjunction POS count to Coordinating Conjunction count |
| 171 | Synta | POSF_ | Part-of-Speech Features | to_CoTag_C | total count of Coordinating Conjunction POS tags |
| 172 | Synta | POSF_ | Part-of-Speech Features | as_CoTag_C | average count of Coordinating Conjunction POS tags per sentence |
| 173 | Synta | POSF_ | Part-of-Speech Features | at_CoTag_C | average count of Coordinating Conjunction POS tags per token |
| 174 | Synta | POSF_ | Part-of-Speech Features | ra_CoAjT_C | ratio of Coordinating Conjunction POS count to Adjective POS count |
| 175 | Synta | POSF_ | Part-of-Speech Features | ra_CoNoT_C | ratio of Coordinating Conjunction POS count to Noun POS count |
| 176 | Synta | POSF_ | Part-of-Speech Features | ra_CoVeT_C | ratio of Coordinating Conjunction POS count to Verb POS count |
| 177 | Synta | POSF_ | Part-of-Speech Features | ra_CoAvT_C | ratio of Coordinating Conjunction POS count to Adverb POS count |
| 178 | Synta | POSF_ | Part-of-Speech Features | ra_CoSuT_C | ratio of Coordinating Conjunction POS count to Subordinating Conjunction count |
| 179 | Synta | POSF_ | Part-of-Speech Features | to_ContW_C | total count of Content words |
| 180 | Synta | POSF_ | Part-of-Speech Features | as_ContW_C | average count of Content words per sentence |
| 181 | Synta | POSF_ | Part-of-Speech Features | at_ContW_C | average count of Content words per token |
| 182 | Synta | POSF_ | Part-of-Speech Features | to_FuncW_C | total count of Function words |
| 183 | Synta | POSF_ | Part-of-Speech Features | as_FuncW_C | average count of Function words per sentence |
| 184 | Synta | POSF_ | Part-of-Speech Features | at_FuncW_C | average count of Function words per token |
| 185 | Synta | POSF_ | Part-of-Speech Features | ra_CoFuW_C | ratio of Content words to Function words |
| 186 | LxSem | VarF_ | Variation Ratio Features | SimpNoV_S | unique Nouns/total Nouns (Noun Variation-1) |
| 187 | LxSem | VarF_ | Variation Ratio Features | SquaNoV_S | (unique Nouns**2)/total Nouns (Squared Noun Variation-1) |
| 188 | LxSem | VarF_ | Variation Ratio Features | CorrNoV_S | unique Nouns/sqrt(2*total Nouns) (Corrected Noun Variation-1) |
| 189 | LxSem | VarF_ | Variation Ratio Features | SimpVeV_S | unique Verbs/total Verbs (Verb Variation-1) |
| 190 | LxSem | VarF_ | Variation Ratio Features | SquaVeV_S | (unique Verbs**2)/total Verbs (Squared Verb Variation-1) |
| 191 | LxSem | VarF_ | Variation Ratio Features | CorrVeV_S | unique Verbs/sqrt(2*total Verbs) (Corrected Verb Variation-1) |
| 192 | LxSem | VarF_ | Variation Ratio Features | SimpAjV_S | unique Adjectives/total Adjectives (Adjective Variation-1) |
| 193 | LxSem | VarF_ | Variation Ratio Features | SquaAjV_S | (unique Adjectives**2)/total Adjectives (Squared Adjective Variation-1) |
| 194 | LxSem | VarF_ | Variation Ratio Features | CorrAjV_S | unique Adjectives/sqrt(2*total Adjectives) (Corrected Adjective Variation-1) |
| 195 | LxSem | VarF_ | Variation Ratio Features | SimpAvV_S | unique Adverbs/total Adverbs (AdVerb Variation-1) |
| 196 | LxSem | VarF_ | Variation Ratio Features | SquaAvV_S | (unique Adverbs**2)/total Adverbs (Squared AdVerb Variation-1) |
| 197 | LxSem | VarF_ | Variation Ratio Features | CorrAvV_S | unique Adverbs/sqrt(2*total Adverbs) (Corrected AdVerb Variation-1) |
| 198 | LxSem | TTRF_ | Type Token Ratio Features | SimpTTR_S | unique tokens/total tokens (TTR) |
| 199 | LxSem | TTRF_ | Type Token Ratio Features | CorrTTR_S | unique tokens/sqrt(2*total tokens) (Corrected TTR) |
| 200 | LxSem | TTRF_ | Type Token Ratio Features | BiLoTTR_S | log(unique tokens)/log(total tokens) (Bi-Logarithmic TTR) |
| 201 | LxSem | TTRF_ | Type Token Ratio Features | UberTTR_S | (log(unique tokens))^2/log(total tokens/unique tokens) (Uber Index) |
| 202 | LxSem | TTRF_ | Type Token Ratio Features | MTLDTTR_S | Measure of Textual Lexical Diversity (default TTR = 0.72) |
| 203 | LxSem | PsyF_ | Psycholinguistic Features | to_AAKuW_C | total AoA (Age of Acquisition) of words |
| 204 | LxSem | PsyF_ | Psycholinguistic Features | as_AAKuW_C | average AoA of words per sentence |
| 205 | LxSem | PsyF_ | Psycholinguistic Features | at_AAKuW_C | average AoA of words per token |
| 206 | LxSem | PsyF_ | Psycholinguistic Features | to_AAKuL_C | total lemmas AoA of lemmas |
| 207 | LxSem | PsyF_ | Psycholinguistic Features | as_AAKuL_C | average lemmas AoA of lemmas per sentence |
| 208 | LxSem | PsyF_ | Psycholinguistic Features | at_AAKuL_C | average lemmas AoA of lemmas per token |
| 209 | LxSem | PsyF_ | Psycholinguistic Features | to_AABiL_C | total lemmas AoA of lemmas, Bird norm |
| 210 | LxSem | PsyF_ | Psycholinguistic Features | as_AABiL_C | average lemmas AoA of lemmas, Bird norm per sentence |
| 211 | LxSem | PsyF_ | Psycholinguistic Features | at_AABiL_C | average lemmas AoA of lemmas, Bird norm per token |
| 212 | LxSem | PsyF_ | Psycholinguistic Features | to_AABrL_C | total lemmas AoA of lemmas, Bristol norm |
| 213 | LxSem | PsyF_ | Psycholinguistic Features | as_AABrL_C | average lemmas AoA of lemmas, Bristol norm per sentence |
| 214 | LxSem | PsyF_ | Psycholinguistic Features | at_AABrL_C | average lemmas AoA of lemmas, Bristol norm per token |
| 215 | LxSem | PsyF_ | Psycholinguistic Features | to_AACoL_C | total AoA of lemmas, Cortese and Khanna norm |
| 216 | LxSem | PsyF_ | Psycholinguistic Features | as_AACoL_C | average AoA of lemmas, Cortese and Khanna norm per sentence |
| 217 | LxSem | PsyF_ | Psycholinguistic Features | at_AACoL_C | average AoA of lemmas, Cortese and Khanna norm per token |
| 218 | LxSem | WorF_ | Word Familiarity | to_SbFrQ_C | total SubtlexUS FREQcount value |
| 219 | LxSem | WorF_ | Word Familiarity | as_SbFrQ_C | average SubtlexUS FREQcount value per sentenc |
| 220 | LxSem | WorF_ | Word Familiarity | at_SbFrQ_C | average SubtlexUS FREQcount value per token |
| 221 | LxSem | WorF_ | Word Familiarity | to_SbCDC_C | total SubtlexUS CDcount value |
| 222 | LxSem | WorF_ | Word Familiarity | as_SbCDC_C | average SubtlexUS CDcount value per sentence |
| 223 | LxSem | WorF_ | Word Familiarity | at_SbCDC_C | average SubtlexUS CDcount value per token |
| 224 | LxSem | WorF_ | Word Familiarity | to_SbFrL_C | total SubtlexUS FREQlow value |
| 225 | LxSem | WorF_ | Word Familiarity | as_SbFrL_C | average SubtlexUS FREQlow value per sentence |
| 226 | LxSem | WorF_ | Word Familiarity | at_SbFrL_C | average SubtlexUS FREQlow value per token |
| 227 | LxSem | WorF_ | Word Familiarity | to_SbCDL_C | total SubtlexUS CDlow value |
| 228 | LxSem | WorF_ | Word Familiarity | as_SbCDL_C | average SubtlexUS CDlow value per sentence |
| 229 | LxSem | WorF_ | Word Familiarity | at_SbCDL_C | average SubtlexUS CDlow value per token |
| 230 | LxSem | WorF_ | Word Familiarity | to_SbSBW_C | total SubtlexUS SUBTLWF value |
| 231 | LxSem | WorF_ | Word Familiarity | as_SbSBW_C | average SubtlexUS SUBTLWF value per sentence |
| 232 | LxSem | WorF_ | Word Familiarity | at_SbSBW_C | average SubtlexUS SUBTLWF value per token |
| 233 | LxSem | WorF_ | Word Familiarity | to_SbL1W_C | total SubtlexUS Lg10WF value |
| 234 | LxSem | WorF_ | Word Familiarity | as_SbL1W_C | average SubtlexUS Lg10WF value per sentence |
| 235 | LxSem | WorF_ | Word Familiarity | at_SbL1W_C | average SubtlexUS Lg10WF value per token |
| 236 | LxSem | WorF_ | Word Familiarity | to_SbSBC_C | total SubtlexUS SUBTLCD value |
| 237 | LxSem | WorF_ | Word Familiarity | as_SbSBC_C | average SubtlexUS SUBTLCD value per sentence |
| 238 | LxSem | WorF_ | Word Familiarity | at_SbSBC_C | average SubtlexUS SUBTLCD value per token |
| 239 | LxSem | WorF_ | Word Familiarity | to_SbL1C_C | total SubtlexUS Lg10CD value |
| 240 | LxSem | WorF_ | Word Familiarity | as_SbL1C_C | average SubtlexUS Lg10CD value per sentence |
| 241 | LxSem | WorF_ | Word Familiarity | at_SbL1C_C | average SubtlexUS Lg10CD value per token |
| 242 | ShaTr | ShaF_ | Shallow Features | TokSenM_S | total count of tokens x total count of sentence |
| 243 | ShaTr | ShaF_ | Shallow Features | TokSenS_S | sqrt(total count of tokens x total count of sentence) |
| 244 | ShaTr | ShaF_ | Shallow Features | TokSenL_S | log(total count of tokens)/log(total count of sentence) |
| 245 | ShaTr | ShaF_ | Shallow Features | as_Token_C | average count of tokens per sentence |
| 246 | ShaTr | ShaF_ | Shallow Features | as_Sylla_C | average count of syllables per sentence |
| 247 | ShaTr | ShaF_ | Shallow Features | at_Sylla_C | average count of syllables per token |
| 248 | ShaTr | ShaF_ | Shallow Features | as_Chara_C | average count of characters per sentence |
| 249 | ShaTr | ShaF_ | Shallow Features | at_Chara_C | average count of characters per token |
| 250 | ShaTr | TraF_ | Traditional Formulas | SmogInd_S | Smog Index |
| 251 | ShaTr | TraF_ | Traditional Formulas | ColeLia_S | Coleman Liau Readability Score |
| 252 | ShaTr | TraF_ | Traditional Formulas | Gunning_S | Gunning Fog Count Score |
| 253 | ShaTr | TraF_ | Traditional Formulas | AutoRea_S | New Automated Readability Index |
| 254 | ShaTr | TraF_ | Traditional Formulas | FleschG_S | Flesch Kincaid Grade Level |
| 255 | ShaTr | TraF_ | Traditional Formulas | LinseaW_S | Linsear Write Formula Score |
## Key References
We list only the key references here. Check our paper for full references.
**Entity Density Features**
> Feng, Lijun, Noémie Elhadad, and Matt Huenerfauth. "Cognitively motivated features for readability assessment." Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009). 2009.
**Entity Grid Features**
> Palma, Diego, and John Atkinson. "Coherence-based automatic essay assessment." IEEE Intelligent Systems 33.5 (2018): 26-36.
Repository: <https://github.com/dpalmasan/TRUNAJOD2.0>
**Variation Features**
> Lu, Xiaofei. "A corpus‐based evaluation of syntactic complexity measures as indices of college‐level ESL writers' language development." TESOL quarterly 45.1 (2011): 36-62.
**Psycholinguistic Features**
> Kuperman, Victor, Hans Stadthagen-Gonzalez, and Marc Brysbaert. "Age-of-acquisition ratings for 30,000 English words." Behavior research methods 44.4 (2012): 978-990.
**Traditional Formulas**
> Kincaid, J. Peter, Robert P. Fishburne Jr, Richard L. Rogers, and Brad S. Chissom. Derivation of new readability formulas (automated readability index, fog count and flesch reading ease formula) for navy enlisted personnel. Naval Technical Training Command Millington TN Research Branch, 1975.
## License
We license LingFeat source code under [Creative Commons Attribution Share Alike 4.0 license (CC-BY-SA-4.0)](https://creativecommons.org/licenses/by-sa/4.0/legalcode).
Under CC-BY-SA-4.0 license, you are allowed to distribute, modify, or privately use this repository.
But patent use, trademark use, and warranty use are not permitted. **Research, code, or etc. that builds on top of LingFeat must be released under the same license.** Alternatively, **a similar open source license may be used.**
## Acknowledgement
I made this software during my time at LXPER AI, Seoul, South Korea. We decided to make this software openly available strictly for academic research purpose only.
I thank the firm and all other members of the R&D department for making this research possible.
[cc-by-sa]: http://creativecommons.org/licenses/by-sa/4.0/
[cc-by-sa-image]: https://licensebuttons.net/l/by-sa/4.0/88x31.png
[cc-by-sa-shield]: https://img.shields.io/badge/License-CC%20BY--SA%204.0-lightgrey.svg
================================================
FILE: lingfeat/_AdvancedSemantic/OSKF.py
================================================
# -*- coding: UTF-8 -*-
"""
Software: LingFeat - Comprehensive Linguistic Features for Readability Assessment
Page: OSKF.py (OneStopEnglish Knowledge Features)
License: CC-BY-SA 4.0
Original Author: Bruce W. Lee (이웅성) @brucewlee
Affiliation 1: LXPER AI, Seoul, South Korea
Affiliation 2: University of Pennsylvania, PA, USA
Contributing Author: -
Affiliation : -
"""
import gensim
from lingfeat.utils import division
def richness(probability_list):
sum_ = 0
for idx, number in enumerate(probability_list):
sum_ += float((idx+1)*number)
return sum_
def clarity(probability_list):
sum_ = 0
for number in probability_list[1:]:
sum_ += (probability_list[0] - number)
result = division(sum_,len(probability_list))
if len(probability_list) == 1:
result = probability_list[0]
return result
def noise(probability_list):
mean = float(1.0/len(probability_list))
s2 = 0
s4 = 0
for number in probability_list:
s2 += (number - mean)**2
s4 += (number - mean)**4
result = len(probability_list)*(division(s4,(s2)**2))
return result
def get_probability_lists(token_list, dir_path):
n_topic_list = [50, 100, 150, 200]
# lda_list is a list of Gensim lda objects
lda_list = []
# probability_lists is a list of list.
# [[probability list at topic 50],[probability list at topic 100],[probability list at topic 150],[probability list at topic 200]]
probability_lists = []
common_dictionary = gensim.corpora.dictionary.Dictionary([token_list])
common_corpus = [common_dictionary.doc2bow(token_list)]
for n_topic in n_topic_list:
lda_list.append(gensim.models.ldamodel.LdaModel.load(dir_path+"/_AdvancedSemantic/model/onestop" + str(n_topic)))
probability_list = []
for lda in lda_list:
for topic_probability in lda.get_document_topics(common_corpus)[0]:
probability_list.append(topic_probability[1])
probability_list = sorted(probability_list, reverse=True)
probability_lists.append(probability_list)
return probability_lists
def retrieve(token_list, dir_path):
n_topic_list_for_naming = ["05", "10", "15", "20"]
probability_lists = get_probability_lists(token_list, dir_path)
# obtain each feature list
richness_list = []
clarity_list = []
noise_list = []
n_topics_list = []
for probability_list in probability_lists:
richness_list.append(richness(probability_list))
clarity_list.append(clarity(probability_list))
noise_list.append(noise(probability_list))
n_topics_list.append(len(probability_list))
# make resulting dictionary
result = {}
for i,feature in enumerate(richness_list):
result["ORich"+n_topic_list_for_naming[i]+"_S"] = feature
for i,feature in enumerate(clarity_list):
result["OClar"+n_topic_list_for_naming[i]+"_S"] = feature
for i,feature in enumerate(noise_list):
result["ONois"+n_topic_list_for_naming[i]+"_S"] = feature
for i,feature in enumerate(n_topics_list):
result["OTopc"+n_topic_list_for_naming[i]+"_S"] = feature
return result
================================================
FILE: lingfeat/_AdvancedSemantic/WBKF.py
================================================
# -*- coding: UTF-8 -*-
"""
Software: LingFeat - Comprehensive Linguistic Features for Readability Assessment
Page: WBKF.py (WeeBit Knowledge Features)
License: CC-BY-SA 4.0
Original Author: Bruce W. Lee (이웅성) @brucewlee
Affiliation 1: LXPER AI, Seoul, South Korea
Affiliation 2: University of Pennsylvania, PA, USA
Contributing Author: -
Affiliation : -
"""
import gensim
from lingfeat.utils import division
def richness(probability_list):
sum_ = 0
for idx, number in enumerate(probability_list):
sum_ += float((idx+1)*number)
return sum_
def clarity(probability_list):
sum_ = 0
for number in probability_list[1:]:
sum_ += (probability_list[0] - number)
result = division(sum_,len(probability_list))
if len(probability_list) == 1:
result = probability_list[0]
return result
def noise(probability_list):
mean = float(1.0/len(probability_list))
s2 = 0
s4 = 0
for number in probability_list:
s2 += (number - mean)**2
s4 += (number - mean)**4
result = len(probability_list)*(division(s4,(s2)**2))
return result
def get_probability_lists(token_list, dir_path):
n_topic_list = [50, 100, 150, 200]
# lda_list is a list of Gensim lda objects
lda_list = []
# probability_lists is a list of list.
# [[probability list at topic 50],[probability list at topic 100],[probability list at topic 150],[probability list at topic 200]]
probability_lists = []
common_dictionary = gensim.corpora.dictionary.Dictionary([token_list])
common_corpus = [common_dictionary.doc2bow(token_list)]
for n_topic in n_topic_list:
lda_list.append(gensim.models.ldamodel.LdaModel.load(dir_path+"/_AdvancedSemantic/model/weebit" + str(n_topic)))
probability_list = []
for lda in lda_list:
for topic_probability in lda.get_document_topics(common_corpus)[0]:
probability_list.append(topic_probability[1])
probability_list = sorted(probability_list, reverse=True)
probability_lists.append(probability_list)
return probability_lists
def retrieve(token_list, dir_path):
n_topic_list_for_naming = ["05", "10", "15", "20"]
probability_lists = get_probability_lists(token_list, dir_path)
# obtain each feature list
richness_list = []
clarity_list = []
noise_list = []
n_topics_list = []
for probability_list in probability_lists:
richness_list.append(richness(probability_list))
clarity_list.append(clarity(probability_list))
noise_list.append(noise(probability_list))
n_topics_list.append(len(probability_list))
# make resulting dictionary
result = {}
for i,feature in enumerate(richness_list):
result["BRich"+n_topic_list_for_naming[i]+"_S"] = feature
for i,feature in enumerate(clarity_list):
result["BClar"+n_topic_list_for_naming[i]+"_S"] = feature
for i,feature in enumerate(noise_list):
result["BNois"+n_topic_list_for_naming[i]+"_S"] = feature
for i,feature in enumerate(n_topics_list):
result["BTopc"+n_topic_list_for_naming[i]+"_S"] = feature
return result
================================================
FILE: lingfeat/_AdvancedSemantic/WoKF.py
================================================
# -*- coding: UTF-8 -*-
"""
Software: LingFeat - Comprehensive Linguistic Features for Readability Assessment
Page: WoKF.py (Wiki Knowledge Features)
License: CC-BY-SA 4.0
Original Author: Bruce W. Lee (이웅성) @brucewlee
Affiliation 1: LXPER AI, Seoul, South Korea
Affiliation 2: University of Pennsylvania, PA, USA
Contributing Author: -
Affiliation : -
"""
import gensim
from lingfeat.utils import division
def richness(probability_list):
sum_ = 0
for idx, number in enumerate(probability_list):
sum_ += float((idx+1)*number)
return sum_
def clarity(probability_list):
sum_ = 0
for number in probability_list[1:]:
sum_ += (probability_list[0] - number)
result = division(sum_,len(probability_list))
if len(probability_list) == 1:
result = probability_list[0]
return result
def noise(probability_list):
mean = float(1.0/len(probability_list))
s2 = 0
s4 = 0
for number in probability_list:
s2 += (number - mean)**2
s4 += (number - mean)**4
result = len(probability_list)*(division(s4,(s2)**2))
return result
def get_probability_lists(token_list, dir_path):
n_topic_list = [50, 100, 150, 200]
# lda_list is a list of Gensim lda objects
lda_list = []
# probability_lists is a list of list.
# [[probability list at topic 50],[probability list at topic 100],[probability list at topic 150],[probability list at topic 200]]
probability_lists = []
common_dictionary = gensim.corpora.dictionary.Dictionary([token_list])
common_corpus = [common_dictionary.doc2bow(token_list)]
for n_topic in n_topic_list:
lda_list.append(gensim.models.ldamodel.LdaModel.load(dir_path+"/_AdvancedSemantic/model/enwiki" + str(n_topic)))
probability_list = []
for lda in lda_list:
for topic_probability in lda.get_document_topics(common_corpus)[0]:
probability_list.append(topic_probability[1])
probability_list = sorted(probability_list, reverse=True)
probability_lists.append(probability_list)
return probability_lists
def retrieve(token_list, dir_path):
n_topic_list_for_naming = ["05", "10", "15", "20"]
probability_lists = get_probability_lists(token_list, dir_path)
# obtain each feature list
richness_list = []
clarity_list = []
noise_list = []
n_topics_list = []
for probability_list in probability_lists:
richness_list.append(richness(probability_list))
clarity_list.append(clarity(probability_list))
noise_list.append(noise(probability_list))
n_topics_list.append(len(probability_list))
# make resulting dictionary
result = {}
for i,feature in enumerate(richness_list):
result["WRich"+n_topic_list_for_naming[i]+"_S"] = feature
for i,feature in enumerate(clarity_list):
result["WClar"+n_topic_list_for_naming[i]+"_S"] = feature
for i,feature in enumerate(noise_list):
result["WNois"+n_topic_list_for_naming[i]+"_S"] = feature
for i,feature in enumerate(n_topics_list):
result["WTopc"+n_topic_list_for_naming[i]+"_S"] = feature
return result
================================================
FILE: lingfeat/_AdvancedSemantic/__init__.py
================================================
================================================
FILE: lingfeat/_AdvancedSemantic/model/__init__.py
================================================
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FILE: lingfeat/_Discourse/EnDF.py
================================================
# -*- coding: UTF-8 -*-
"""
Software: LingFeat - Comprehensive Linguistic Features for Readability Assessment
Page: EnDF.py (Entity Density Features)
License: CC-BY-SA 4.0
Original Author: Bruce W. Lee (이웅성) @brucewlee
Affiliation 1: LXPER AI, Seoul, South Korea
Affiliation 2: University of Pennsylvania, PA, USA
Contributing Author: -
Affiliation : -
References:
>>> Entity Density features inspired by
Publication 1: Feng, Lijun, Noémie Elhadad, and Matt Huenerfauth. "Cognitively motivated features for readability assessment." Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009). 2009.
"""
def retrieve(NLP_doc, n_sent, n_token):
to_EntiM_C = 0
to_UEnti_C = 0
ent_list = []
unique_ent_list = []
for ent in NLP_doc.ents:
to_EntiM_C += 1
ent_list.append(ent.text)
for ent in ent_list:
if ent_list.count(ent) == 1:
to_UEnti_C += 1
unique_ent_list.append(ent)
result = {
"to_EntiM_C": to_EntiM_C,
"as_EntiM_C": to_EntiM_C/n_sent,
"at_EntiM_C": to_EntiM_C/n_token,
"to_UEnti_C": to_UEnti_C,
"as_UEnti_C": to_UEnti_C/n_sent,
"at_UEnti_C": to_UEnti_C/n_token,
}
return result
================================================
FILE: lingfeat/_Discourse/EnGF.py
================================================
# -*- coding: UTF-8 -*-
"""
Software: LingFeat - Comprehensive Linguistic Features for Readability Assessment
Page: EnGF.py (Entity Grid Features)
License: CC-BY-SA 4.0
Original Author: Diego Palma @dpalmasan
Affiliation: Evernote
Contributing Author: Bruce W. Lee (이웅성) @brucewlee
Affiliation 1: LXPER AI, Seoul, South Korea
Affiliation 2: University of Pennsylvania, PA, USA
References:
>>> Original Python Code from TRUNAJOD2.0, modified by Bruce W. Lee
Publication 1: Palma, Diego, and John Atkinson. "Coherence-based automatic essay assessment." IEEE Intelligent Systems 33.5 (2018): 26-36.
Repository: <https://github.com/dpalmasan/TRUNAJOD2.0>
>>> S-V-O extraction inspired by an algorithm from
Publication 1: Rusu, Delia, et al. "Triplet extraction from sentences." Proceedings of the 10th International Multiconference” Information Society-IS. 2007.
>>> 16 transition patterns approach obtained from
Publication 1: Barzilay, Regina, and Mirella Lapata. "Modeling local coherence: An entity-based approach." Computational Linguistics 34.1 (2008): 1-34.
Publication 2: Pitler, Emily, and Ani Nenkova. "Revisiting readability: A unified framework for predicting text quality." Proceedings of the 2008 conference on empirical methods in natural language processing. 2008.
>>> Notations in Dependency Mapping
Publication 1: Barzilay, Regina, and Mirella Lapata. "Modeling local coherence: An entity-based approach." Computational Linguistics 34.1 (2008): 1-34.
>>> Local Coherence (graph-based)
Publication 1: Guinaudeau, Camille, and Michael Strube. "Graph-based local coherence modeling." Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2013.
"""
from lingfeat.utils import division
NOUNS = set(['NOUN', 'PRON', 'PROPN'])
ordered_transitions = [
'SS', 'SO', 'SX', 'S-', 'OS', 'OO', 'OX', 'O-', 'XS', 'XO',
'XX', 'X-', '-S', '-O', '-X', '--'
]
def dependency_mapping(dep):
"""
+-----------+-----------------------------------+
| EGrid Tag | Dependency Tag |
+===========+===================================+
| S | nsub, csubj, csubjpass, dsubjpass |
+-----------+-----------------------------------+
| O | iobj, obj, pobj, dobj |
+-----------+-----------------------------------+
| X | For any other dependency tag |
+-----------+-----------------------------------+
"""
S = ['nsubj', 'csubj', 'csubjpass', 'dsubjpass']
O = ['iobj', 'obj', 'pobj', 'dobj']
if S.count(dep) == 1:
return 'S'
if O.count(dep) == 1:
return 'O'
return 'X'
class EntityGrid:
def __init__(self, NLP_doc, n_sent):
"""Construct EntityGrid object."""
# Initialization
entity_map = dict()
entity_grid = dict()
i = 1
entity_map['s%d' % i] = []
entity_features = {
'SS': 0,
'SO': 0,
'SX': 0,
'S-': 0,
'OS': 0,
'OO': 0,
'OX': 0,
'O-': 0,
'XS': 0,
'XO': 0,
'XX': 0,
'X-': 0,
'-S': 0,
'-O': 0,
'-X': 0,
'--': 0
}
# For each sentence, get dependencies and its grammatical role
for token in NLP_doc:
if token.pos_ in NOUNS:
entity_map['s%d' % i].append((token.text.upper(),
token.dep_))
if token.text.upper() not in entity_grid:
entity_grid[token.text.upper()] = ['-'] * n_sent
i += 1
entity_map['s%d' % i] = []
# Last iteration will create an extra element, so I remove it.
entity_map.pop('s%d' % i)
# Fill entity grid
for i in range(n_sent):
sentence = "s%d" % (i + 1)
for entity, dep in entity_map[sentence]:
if entity_grid[entity][i] == '-':
entity_grid[entity][i] = dependency_mapping(dep)
elif dependency_mapping(dep) == 'S':
entity_grid[entity][i] = dependency_mapping(dep)
elif (dependency_mapping(dep) == 'O'
and entity_grid[entity][i] == 'X'):
entity_grid[entity][i] = dependency_mapping(dep)
# Compute feature vector, we consider transitions of length 2
total_transitions = (n_sent - 1) * len(entity_grid.keys())
for entity in entity_grid:
for i in range(n_sent - 1):
# Transition type found (e.g. S-)
transition = (
entity_grid[entity][i] + entity_grid[entity][i + 1])
# Adding 1 to transition count
entity_features[transition] += 1
for prob in entity_features:
if total_transitions != 0:
entity_features[prob] /= float(total_transitions)
else:
entity_features[prob] = 0.0
self.grid = entity_grid
self.n_sent = n_sent
self.prob = entity_features
def retrieve(self):
"""
Retrieve All
"""
result = {}
result['ra_SSTo_C'] = self.prob['SS']
result['ra_SOTo_C'] = self.prob['SO']
result['ra_SXTo_C'] = self.prob['SX']
result['ra_SNTo_C'] = self.prob['S-']
result['ra_OSTo_C'] = self.prob['OS']
result['ra_OOTo_C'] = self.prob['OO']
result['ra_OXTo_C'] = self.prob['OX']
result['ra_ONTo_C'] = self.prob['O-']
result['ra_XSTo_C'] = self.prob['XS']
result['ra_XOTo_C'] = self.prob['XO']
result['ra_XXTo_C'] = self.prob['XX']
result['ra_XNTo_C'] = self.prob['X-']
result['ra_NSTo_C'] = self.prob['-S']
result['ra_NOTo_C'] = self.prob['-O']
result['ra_NXTo_C'] = self.prob['-X']
result['ra_NNTo_C'] = self.prob['--']
lcresult = get_local_coherence(self)
return {**result, **lcresult}
def weighting_syntactic_role(entity_role):
"""
Return weight given an entity grammatical role.
+-----------+--------+
| EGrid Tag | Weight |
+===========+========+
| S | 3 |
+-----------+--------+
| O | 2 |
+-----------+--------+
| X | 1 |
+-----------+--------+
| dash | 0 |
+-----------+--------+
"""
if entity_role == u"S":
return 3
elif entity_role == u"O":
return 2
elif entity_role == u"X":
return 1
return 0
def get_local_coherence(EntityGrid):
local_coherence_PU = 0.0 #lcPA
local_coherence_PW = 0.0 #lcPW
local_coherence_PA = 0.0 #lcPU
local_coherence_PU_dist = 0.0 #lcdPA
local_coherence_PW_dist = 0.0 #lcdPW
local_coherence_PA_dist = 0.0 #lcdPU
n_sent = EntityGrid.n_sent
grid = EntityGrid.grid
PW = [[0] * n_sent for i in range(n_sent)]
# Weight Matrix for PACC, syntactic information is accounted for by
# integrating the edges of the bipartite graph
W = [[0] * n_sent for i in range(n_sent)]
for entity in grid:
for i in range(n_sent):
for j in range(i + 1, n_sent):
if grid[entity][i] != u"-" and grid[entity][j] != u"-":
PW[i][j] += 1
W[i][j] += (weighting_syntactic_role(grid[entity][i]) *
weighting_syntactic_role(grid[entity][j]))
PU = [list(map(lambda x: x != 0, PWi)) for PWi in PW]
for i in range(n_sent):
local_coherence_PW += sum(PW[i])
local_coherence_PU += sum(PU[i])
local_coherence_PA += sum(W[i])
local_coherence_PW /= n_sent
local_coherence_PU /= n_sent
local_coherence_PA /= n_sent
# Weighting projection graphs
PU_weighted = list(PU)
PW_weighted = list(PW)
PA_weighted = list(W)
for i in range(n_sent):
for j in range(i + 1, n_sent):
PU_weighted[i][j] = division(PU[i][j],float(j - i))
PW_weighted[i][j] = division(PW[i][j],float(j - i))
PA_weighted[i][j] = division(W[i][j],float(j - i))
for i in range(n_sent):
local_coherence_PW_dist += sum(PW_weighted[i])
local_coherence_PU_dist += sum(PU_weighted[i])
local_coherence_PA_dist += sum(PA_weighted[i])
local_coherence_PW_dist /= n_sent
local_coherence_PU_dist /= n_sent
local_coherence_PA_dist /= n_sent
result = {
'LoCohPA_S':local_coherence_PU,
'LoCohPW_S':local_coherence_PW,
'LoCohPU_S':local_coherence_PA,
'LoCoDPA_S':local_coherence_PU_dist,
'LoCoDPW_S':local_coherence_PW_dist,
'LoCoDPU_S':local_coherence_PA_dist,
}
return result
================================================
FILE: lingfeat/_Discourse/__init__.py
================================================
================================================
FILE: lingfeat/_LexicoSemantic/PsyF.py
================================================
# -*- coding: UTF-8 -*-
"""
Software: LingFeat - Comprehensive Linguistic Features for Readability Assessment
Page: PstF.py (Psycholinguistic Features)
License: CC-BY-SA 4.0
Original Author: Bruce W. Lee (이웅성) @brucewlee
Affiliation 1: LXPER AI, Seoul, South Korea
Affiliation 2: University of Pennsylvania, PA, USA
Contributing Author: -
Affiliation : -
References:
>>> Psycholinguistic features inspired by
Publication 1: Kuperman, Victor, Hans Stadthagen-Gonzalez, and Marc Brysbaert. "Age-of-acquisition ratings for 30,000 English words." Behavior research methods 44.4 (2012): 978-990.
Publication 2: Vajjala, Sowmya, and Detmar Meurers. "Readability-based sentence ranking for evaluating text simplification." (2015).
"""
import pandas as pd
def retrieve(token_list, n_token, n_sent, dir_path):
to_AAKuW_C = 0
to_AAKuL_C = 0
to_AABiL_C = 0
to_AABrL_C = 0
to_AACoL_C = 0
DB = pd.read_csv(dir_path+'/_LexicoSemantic/resources/AoAKuperman.csv')
DB.set_index('Word', inplace=True, drop=True)
for token in token_list:
if token in DB.index:
scores_for_this_token = list(DB.loc[token, :])
for i, score in enumerate(scores_for_this_token):
scores_for_this_token[i] = 0 if str(score) == 'none' else scores_for_this_token[i]
to_AAKuW_C += float(scores_for_this_token[7])
to_AAKuL_C += float(scores_for_this_token[9])
to_AABiL_C += float(scores_for_this_token[11])
to_AABrL_C += float(scores_for_this_token[12])
to_AACoL_C += float(scores_for_this_token[13])
result = {
"to_AAKuW_C": to_AAKuW_C,
"as_AAKuW_C": to_AAKuW_C/n_sent ,
"at_AAKuW_C": to_AAKuW_C/n_token,
"to_AAKuL_C": to_AAKuL_C,
"as_AAKuL_C": to_AAKuL_C/n_sent ,
"at_AAKuL_C": to_AAKuL_C/n_token,
"to_AABiL_C": to_AABiL_C,
"as_AABiL_C": to_AABiL_C/n_sent ,
"at_AABiL_C": to_AABiL_C/n_token,
"to_AABrL_C": to_AABrL_C,
"as_AABrL_C": to_AABrL_C/n_sent ,
"at_AABrL_C": to_AABrL_C/n_token,
"to_AACoL_C": to_AABrL_C,
"as_AACoL_C": to_AABrL_C/n_sent ,
"at_AACoL_C": to_AABrL_C/n_token,
}
return result
================================================
FILE: lingfeat/_LexicoSemantic/TTRF.py
================================================
# -*- coding: UTF-8 -*-
"""
Software: LingFeat - Comprehensive Linguistic Features for Readability Assessment
Page: TTRF.py (Type-Token-Ratio Features)
License: CC-BY-SA 4.0
Original Author: Bruce W. Lee (이웅성) @brucewlee
Affiliation 1: LXPER AI, Seoul, South Korea
Affiliation 2: University of Pennsylvania, PA, USA
Contributing Author: -
Affiliation : -
References:
>>> Entity Density features inspired by
Publication 1: Malvern, David, and Brian Richards. "Measures of lexical richness." The encyclopedia of applied linguistics (2012).
"""
import math
from lingfeat.utils import division
def retrieve(n_token,token_list):
n_utoken = 1
default_MTLD = 0.72
MTLD_count = 0
for token in token_list:
if token_list.count(token) == 1:
n_utoken += 1
if float(n_utoken/n_token) >= 0.72:
MTLD_count += 1
result={
"SimpTTR_S":float(n_utoken/n_token),
"CorrTTR_S":float(n_utoken/math.sqrt(2*n_token)),
"BiLoTTR_S":float(math.log(n_utoken)/math.log(n_token)),
"UberTTR_S":float(division(((math.log(n_utoken))**2),(math.log(n_token/n_utoken)))),
"MTLDTTR_S":float(MTLD_count)
}
return result
================================================
FILE: lingfeat/_LexicoSemantic/VarF.py
================================================
# -*- coding: UTF-8 -*-
"""
Software: LingFeat - Comprehensive Linguistic Features for Readability Assessment
Page: VarF.py (Variational Features)
License: CC-BY-SA 4.0
Original Author: Bruce W. Lee (이웅성) @brucewlee
Affiliation 1: LXPER AI, Seoul, South Korea
Affiliation 2: University of Pennsylvania, PA, USA
Contributing Author: -
Affiliation : -
References:
>>> Variational features inspired by
Publication 1: Lu, Xiaofei. "A corpus‐based evaluation of syntactic complexity measures as indices of college‐level ESL writers' language development." TESOL quarterly 45.1 (2011): 36-62.
Publication 2: Vajjala, Sowmya, and Detmar Meurers. "On improving the accuracy of readability classification using insights from second language acquisition." Proceedings of the seventh workshop on building educational applications using NLP. 2012.
"""
import math
from lingfeat.utils import division
def retrieve(NLP_doc):
noun_list = []
verb_list = []
adje_list = []
adve_list = []
n_unoun = 0
n_uverb = 0
n_uadje = 0
n_uadve = 0
for token in NLP_doc:
if token.pos_ == "NOUN":
noun_list.append(token.lemma)
if token.pos_ == "VERB":
verb_list.append(token.lemma)
if token.pos_ == "ADJ":
adje_list.append(token.lemma)
if token.pos_ == "ADV":
adve_list.append(token.lemma)
for noun in noun_list:
if noun_list.count(noun) == 1:
n_unoun += 1
for verb in verb_list:
if verb_list.count(verb) == 1:
n_uverb += 1
for adje in adje_list:
if adje_list.count(adje) == 1:
n_uadje += 1
for adve in adve_list:
if adve_list.count(adve) == 1:
n_uadve += 1
result = {
"SimpNoV_S":float(division(n_unoun,(len(noun_list)))),
"SquaNoV_S":float(division((n_unoun)**2,(len(noun_list)))),
"CorrNoV_S":float(division(n_unoun,(math.sqrt(2*len(noun_list))))),
"SimpVeV_S":float(division(n_uverb,(len(verb_list)))),
"SquaVeV_S":float(division((n_uverb)**2,(len(verb_list)))),
"CorrVeV_S":float(division(n_uverb,(math.sqrt(2*len(verb_list))))),
"SimpAjV_S":float(division(n_uadje,(len(adje_list)))),
"SquaAjV_S":float(division((n_uadje)**2,(len(adje_list)))),
"CorrAjV_S":float(division(n_uadje,(math.sqrt(2*len(adje_list))))),
"SimpAvV_S":float(division(n_uadve,(len(adve_list)))),
"SquaAvV_S":float(division((n_uadve)**2,(len(adve_list)))),
"CorrAvV_S":float(division(n_uadve,(math.sqrt(2*len(adve_list))))),
}
return result
================================================
FILE: lingfeat/_LexicoSemantic/WorF.py
================================================
# -*- coding: UTF-8 -*-
"""
Software: LingFeat - Comprehensive Linguistic Features for Readability Assessment
Page: WorF.py (Word Frequency Features)
License: CC-BY-SA 4.0
Original Author: Bruce W. Lee (이웅성) @brucewlee
Affiliation 1: LXPER AI, Seoul, South Korea
Affiliation 2: University of Pennsylvania, PA, USA
Contributing Author: -
Affiliation : -
References:
>>> Word Frequency features inspired by
Publication 1: Vajjala, Sowmya, and Detmar Meurers. "Readability-based sentence ranking for evaluating text simplification." (2015).
"""
import pandas as pd
def retrieve(token_list, n_token, n_sent, dir_path):
to_SbFrQ_C = 0
to_SbCDC_C = 0
to_SbFrL_C = 0
to_SbCDL_C = 0
to_SbSBW_C = 0
to_SbL1W_C = 0
to_SbSBC_C = 0
to_SbL1C_C = 0
DB = pd.read_csv(dir_path+'/_LexicoSemantic/resources/SUBTLEXus.csv')
DB.set_index('Word_lowercased', inplace=True, drop=True)
for token in token_list:
if token in DB.index:
scores_for_this_token = list(DB.loc[token, :])
for i, score in enumerate(scores_for_this_token):
scores_for_this_token[i] = 0 if str(score) == 'none' else scores_for_this_token[i]
to_SbFrQ_C += float(scores_for_this_token[1])
to_SbCDC_C += float(scores_for_this_token[2])
to_SbFrL_C += float(scores_for_this_token[3])
to_SbCDL_C += float(scores_for_this_token[4])
to_SbSBW_C += float(scores_for_this_token[5])
to_SbL1W_C += float(scores_for_this_token[6])
to_SbSBC_C += float(scores_for_this_token[7])
to_SbL1C_C += float(scores_for_this_token[8])
result = {
"to_SbFrQ_C": to_SbFrQ_C,
"as_SbFrQ_C": to_SbFrQ_C/n_sent ,
"at_SbFrQ_C": to_SbFrQ_C/n_token,
"to_SbCDC_C": to_SbCDC_C,
"as_SbCDC_C": to_SbCDC_C/n_sent ,
"at_SbCDC_C": to_SbCDC_C/n_token,
"to_SbFrL_C": to_SbFrL_C,
"as_SbFrL_C": to_SbFrL_C/n_sent ,
"at_SbFrL_C": to_SbFrL_C/n_token,
"to_SbCDL_C": to_SbCDL_C,
"as_SbCDL_C": to_SbCDL_C/n_sent ,
"at_SbCDL_C": to_SbCDL_C/n_token,
"to_SbSBW_C": to_SbSBW_C,
"as_SbSBW_C": to_SbSBW_C/n_sent ,
"at_SbSBW_C": to_SbSBW_C/n_token,
"to_SbL1W_C": to_SbL1W_C,
"as_SbL1W_C": to_SbL1W_C/n_sent ,
"at_SbL1W_C": to_SbL1W_C/n_token,
"to_SbSBC_C": to_SbSBC_C,
"as_SbSBC_C": to_SbSBC_C/n_sent ,
"at_SbSBC_C": to_SbSBC_C/n_token,
"to_SbL1C_C": to_SbL1C_C,
"as_SbL1C_C": to_SbL1C_C/n_sent ,
"at_SbL1C_C": to_SbL1C_C/n_token,
}
return result
================================================
FILE: lingfeat/_LexicoSemantic/__init__.py
================================================
================================================
FILE: lingfeat/_LexicoSemantic/resources/AoAKuperman.csv
================================================
Word,Alternative,Freq_pm,Dom_PoS_SUBTLEX,Nletters,Nphon,Nsyll,Lemma_highest_PoS,AoA_Kup,Perc_known,AoA_Kup_lem,Perc_known_lem,AoA_Bird_lem,AoA_Bristol_lem,AoA_Cort_lem,AoA_Schock
a,a,20415.27,Article,1,1,1,a,2.89,1.00,2.89,1.00,3.16,none,none,
aardvark,aardvark,0.41,Noun,8,7,2,aardvark,9.89,1.00,9.89,1.00,none,none,none,
abacus,abacus,0.24,Noun,6,6,3,abacus,8.69,0.65,8.69,0.65,none,none,none,
abacuses,abacuses,0.02,Noun,8,9,4,abacus,none,none,8.69,0.65,none,none,none,
abalone,abalone,0.51,Verb,7,7,4,abalone,12.23,0.72,12.23,0.72,none,none,none,
abalones,abalones,0.12,Noun,8,7,3,abalone,none,none,12.23,0.72,none,none,none,
abandon,abandon,8.10,Verb,7,7,3,abandon,8.32,1.00,8.32,1.00,none,none,none,
abandoned,abandoned,13.29,Verb,9,8,3,abandon,none,none,8.32,1.00,none,none,none,
abandoner,abandoner,0.02,Noun,9,8,4,abandoner,11.89,0.95,11.89,0.95,none,none,none,
abandoning,abandoning,1.69,Verb,10,9,4,abandon,none,none,8.32,1.00,none,none,none,
abandonment,abandonment,0.96,Noun,11,11,4,abandonment,10.27,1.00,10.27,1.00,none,10.70,none,
abandons,abandons,0.27,Verb,8,8,3,abandon,none,none,8.32,1.00,none,none,none,
abase,abase,0.06,Verb,5,4,2,abase,14.57,0.74,14.57,0.74,none,none,none,
abasement,abasement,0.00,Err:512,9,8,3,abasement,15.13,0.63,15.13,0.63,none,none,none,
abate,abate,0.10,Verb,5,4,2,abate,14.44,0.95,14.44,0.95,11.93,none,none,
abated,abated,0.08,Verb,6,6,3,abate,none,none,14.44,0.95,11.93,none,none,
abatement,abatement,0.06,Noun,9,8,3,abatement,15.12,0.81,15.12,0.81,none,12.58,none,
abates,abates,0.04,Verb,6,5,2,abate,none,none,14.44,0.95,11.93,none,none,
abating,abating,0.02,Verb,7,6,3,abate,none,none,14.44,0.95,11.93,none,none,
abattoir,abattoir,0.14,Noun,8,6,3,abattoir,15.17,0.32,15.17,0.32,none,none,none,
abbacy,abbacy,Err:512,Err:512,6,5,3,abbacy,14.50,0.10,14.50,0.10,none,none,none,
abbess,abbess,0.04,Noun,6,4,2,abbess,15.43,0.37,15.43,0.37,none,none,none,
abbesses,abbesses,0.02,Noun,8,6,3,abbess,none,none,15.43,0.37,none,none,none,
abbey,abbey,3.18,Noun,5,3,2,abbey,13.06,0.86,13.06,0.86,none,9.49,none,11.29
abbot,abbot,0.84,Name,5,4,2,abbot,12.10,0.75,12.10,0.75,none,none,none,12.43
abbreviate,abbreviate,0.02,Verb,10,8,4,abbreviate,9.95,1.00,9.95,1.00,none,none,none,
abbreviated,abbreviated,0.31,Adjective,11,10,5,abbreviated,10.50,1.00,10.50,1.00,none,none,none,
abbreviation,abbreviation,0.24,Noun,12,10,5,abbreviation,9.11,1.00,9.11,1.00,none,none,none,
abbreviations,abbreviations,0.20,Noun,13,11,5,abbreviation,none,none,9.11,1.00,none,none,none,
abdicate,abdicate,0.18,Verb,8,7,3,abdicate,12.60,0.83,12.60,0.83,none,none,none,
abdicated,abdicated,0.14,Verb,9,9,4,abdicate,none,none,12.60,0.83,none,none,none,
abdicates,abdicates,0.02,Noun,9,8,3,abdicate,none,none,12.60,0.83,none,none,none,
abdicating,abdicating,0.06,Verb,10,9,4,abdicate,none,none,12.60,0.83,none,none,none,
abdication,abdication,0.12,Noun,10,9,4,abdication,14.94,0.89,14.94,0.89,none,none,none,
abdomen,abdomen,3.35,Noun,7,7,3,abdomen,8.61,1.00,8.61,1.00,none,10.92,none,
abdomens,abdomens,0.06,Noun,8,8,3,abdomen,none,none,8.61,1.00,none,10.92,none,
abdominal,abdominal,3.41,Adjective,9,9,4,abdominal,10.24,0.94,10.24,0.94,none,none,none,
abduct,abduct,0.71,Verb,6,6,2,abduct,11.26,1.00,11.26,1.00,none,none,none,9.49
abducted,abducted,5.00,Verb,8,8,3,abduct,none,none,11.26,1.00,none,none,none,9.49
abducting,abducting,0.31,Verb,9,8,3,abduct,none,none,11.26,1.00,none,none,none,9.49
abduction,abduction,3.63,Noun,9,8,3,abduction,11.94,1.00,11.94,1.00,none,none,none,
abductions,abductions,0.82,Noun,10,9,3,abduction,none,none,11.94,1.00,none,none,none,
abductor,abductor,0.35,Noun,8,7,3,abductor,11.11,1.00,11.11,1.00,none,none,none,
abductors,abductors,0.22,Noun,9,8,3,abductor,none,none,11.11,1.00,none,none,none,
abducts,abducts,0.16,Verb,7,7,2,abduct,none,none,11.26,1.00,none,none,none,9.49
abeam,abeam,0.14,Noun,5,4,2,abeam,13.40,0.53,13.40,0.53,none,none,none,
aberrant,aberrant,0.29,Adjective,8,7,3,aberrant,13.31,0.68,13.31,0.68,none,none,none,
aberration,aberration,0.55,Noun,10,7,4,aberration,12.69,0.68,12.69,0.68,none,none,none,
aberrations,aberrations,0.16,Noun,11,8,4,aberration,none,none,12.69,0.68,none,none,none,
abet,abet,0.10,Verb,4,4,2,abet,12.15,0.72,12.15,0.72,none,none,none,
abetted,abetted,0.16,Verb,7,6,3,abet,none,none,12.15,0.72,none,none,none,
abetting,abetting,0.51,Verb,8,6,3,abet,none,none,12.15,0.72,none,none,none,
abettor,abettor,0.02,Noun,7,5,3,abettor,15.55,0.58,15.55,0.58,none,none,none,
abeyance,abeyance,0.04,Noun,8,6,3,abeyance,15.00,0.35,15.00,0.35,none,none,none,
abhor,abhor,0.24,Verb,5,5,2,abhor,13.76,0.85,13.76,0.85,none,none,none,
abhorred,abhorred,0.14,Verb,8,6,2,abhor,none,none,13.76,0.85,none,none,none,
abhorrent,abhorrent,0.18,Adjective,9,8,3,abhorrent,12.14,0.78,12.14,0.78,none,none,none,
abhors,abhors,0.08,Verb,6,6,2,abhor,none,none,13.76,0.85,none,none,none,
abide,abide,2.71,Verb,5,4,2,abide,9.50,1.00,9.50,1.00,none,10.49,none,
abided,abided,0.06,Verb,6,6,3,abide,none,none,9.50,1.00,none,10.49,none,
abides,abides,0.08,Verb,6,5,2,abide,none,none,9.50,1.00,none,10.49,none,
abiding,abiding,0.49,Adjective,7,6,3,abiding,10.30,1.00,10.30,1.00,none,none,none,
abilities,abilities,6.12,Noun,9,8,4,ability,none,none,8.84,1.00,none,8.98,none,
ability,ability,19.22,Noun,7,7,4,ability,8.84,1.00,8.84,1.00,none,8.98,none,
abject,abject,0.39,Adjective,6,6,2,abject,14.00,0.89,14.00,0.89,none,none,none,
abjuration,abjuration,Err:512,Err:512,10,9,4,abjuration,17.12,0.38,17.12,0.38,none,none,none,
abjure,abjure,0.02,Verb,6,5,2,abjure,14.60,0.53,14.60,0.53,none,none,none,
ablation,ablation,0.02,Noun,8,7,3,ablation,13.29,0.39,13.29,0.39,none,none,none,
ablaze,ablaze,0.41,Adjective,6,5,2,ablaze,10.83,0.95,10.83,0.95,none,none,none,
able,able,159.90,Adjective,4,4,2,able,7.79,1.00,7.79,1.00,5.71,6.50,none,6.33
abler,abler,0.02,Adjective,5,5,3,able,none,none,7.79,1.00,5.71,6.50,none,6.33
ablest,ablest,0.04,Adjective,6,6,2,able,none,none,7.79,1.00,5.71,6.50,none,6.33
ablution,ablution,0.06,Noun,8,9,4,ablution,13.64,0.58,13.64,0.58,none,none,none,
ablutions,ablutions,0.08,Noun,9,9,4,ablution,none,none,13.64,0.58,none,none,none,
abnormal,abnormal,2.90,Adjective,8,8,3,abnormal,10.05,1.00,10.05,1.00,none,none,none,
abnormalities,abnormalities,0.49,Noun,13,12,5,abnormality,none,none,11.58,1.00,none,none,none,
abnormality,abnormality,0.51,Noun,11,11,5,abnormality,11.58,1.00,11.58,1.00,none,none,none,
aboard,aboard,26.63,Adverb,6,5,2,aboard,8.66,1.00,8.66,1.00,none,6.94,none,8.37
abode,abode,0.65,Noun,5,4,2,abode,12.06,0.94,12.06,0.94,none,10.87,none,12.11
abolish,abolish,0.47,Verb,7,6,3,abolish,10.42,0.95,10.42,0.95,none,none,none,
abolished,abolished,0.39,Verb,9,7,3,abolish,none,none,10.42,0.95,none,none,none,
abolishes,abolishes,0.02,Verb,9,8,4,abolish,none,none,10.42,0.95,none,none,none,
abolishing,abolishing,0.04,Verb,10,8,4,abolish,none,none,10.42,0.95,none,none,none,
abolition,abolition,0.20,Noun,9,8,4,abolition,13.28,1.00,13.28,1.00,none,none,none,
abolitionism,abolitionism,0.02,Noun,12,12,6,abolitionism,11.78,0.95,11.78,0.95,none,none,none,
abolitionist,abolitionist,0.22,Noun,12,11,5,abolitionist,12.60,0.83,12.60,0.83,none,none,none,
abolitionists,abolitionists,0.25,Noun,13,12,5,abolitionist,none,none,12.60,0.83,none,none,none,
abominable,abominable,0.63,Adjective,10,10,5,abominable,8.94,1.00,8.94,1.00,none,none,none,
abominate,abominate,0.06,Verb,9,8,4,abominate,15.00,0.89,15.00,0.89,none,none,none,
abomination,abomination,1.14,Noun,11,10,5,abomination,11.17,1.00,11.17,1.00,none,none,none,
abominations,abominations,0.14,Noun,12,12,6,abomination,none,none,11.17,1.00,none,none,none,
aboriginal,aboriginal,0.18,Adjective,10,9,5,aboriginal,14.75,0.84,14.75,0.84,none,none,none,
aboriginals,aboriginals,0.02,Name,11,11,5,aboriginal,none,none,14.75,0.84,none,none,none,
aborigine,aborigine,0.08,Noun,9,8,5,aborigine,12.56,0.89,12.56,0.89,none,none,none,
aborigines,aborigines,0.18,Noun,10,9,5,aborigine,none,none,12.56,0.89,none,none,none,
aborning,aborning,0.02,Verb,8,7,3,aborning,13.25,0.24,13.25,0.24,none,none,none,
abort,abort,4.61,Verb,5,5,2,abort,12.21,1.00,12.21,1.00,none,none,none,10.04
aborted,aborted,0.63,Verb,7,7,3,abort,none,none,12.21,1.00,none,none,none,10.04
aborting,aborting,0.25,Verb,8,7,3,abort,none,none,12.21,1.00,none,none,none,10.04
abortion,abortion,7.24,Noun,8,7,3,abortion,13.21,1.00,13.21,1.00,none,none,none,
abortionist,abortionist,0.08,Noun,11,10,4,abortionist,15.00,0.95,15.00,0.95,none,none,none,
abortionists,abortionists,0.02,Noun,12,11,4,abortionist,none,none,15.00,0.95,none,none,none,
abortions,abortions,0.69,Noun,9,8,3,abortion,none,none,13.21,1.00,none,none,none,
abortive,abortive,0.02,Adjective,8,7,3,abortive,13.93,0.78,13.93,0.78,none,none,none,
aborts,aborts,0.02,Verb,6,6,2,abort,none,none,12.21,1.00,none,none,none,10.04
abound,abound,0.35,Verb,6,5,2,abound,11.65,1.00,11.65,1.00,none,none,none,
abounded,abounded,0.06,Verb,8,7,3,abound,none,none,11.65,1.00,none,none,none,
abounding,abounding,0.08,Verb,9,7,3,abound,none,none,11.65,1.00,none,none,none,
abounds,abounds,0.20,Verb,7,6,2,abound,none,none,11.65,1.00,none,none,none,
about,about,3631.49,Preposition,5,4,2,about,5.07,1.00,5.07,1.00,4.81,none,none,6.00
above,above,48.88,Preposition,5,4,2,above,4.55,1.00,4.55,1.00,4.48,none,none,5.56
aboveboard,aboveboard,0.27,Noun,10,8,3,aboveboard,13.00,0.85,13.00,0.85,none,none,none,
aboveground,aboveground,0.14,Adjective,11,9,3,aboveground,8.05,1.00,8.05,1.00,none,none,none,
abovementioned,abovementioned,0.02,Adjective,14,11,4,abovementioned,14.24,0.89,14.24,0.89,none,none,none,
abracadabra,abracadabra,1.04,Noun,11,11,5,abracadabra,6.89,1.00,6.89,1.00,none,none,none,
abrade,abrade,0.02,Verb,6,5,2,abrade,14.00,0.44,14.00,0.44,none,none,none,
abraded,abraded,0.10,Verb,7,7,3,abrade,none,none,14.00,0.44,none,none,none,
abrasion,abrasion,0.35,Noun,8,7,3,abrasion,11.95,0.95,11.95,0.95,none,none,none,
abrasions,abrasions,1.10,Noun,9,8,3,abrasion,none,none,11.95,0.95,none,none,none,
abrasive,abrasive,0.45,Adjective,8,7,3,abrasive,12.22,1.00,12.22,1.00,none,none,none,
abrasiveness,abrasiveness,0.04,Noun,12,10,4,abrasiveness,12.06,1.00,12.06,1.00,none,none,none,
abrasives,abrasives,0.08,Verb,9,8,3,abrasive,none,none,12.22,1.00,none,none,none,
abreaction,abreaction,0.02,Noun,10,9,4,abreaction,15.80,0.56,15.80,0.56,none,none,none,
abreast,abreast,0.51,Adjective,7,6,2,abreast,13.06,0.89,13.06,0.89,none,none,none,13.18
abridge,abridge,0.02,Noun,7,5,2,abridge,14.35,0.89,14.35,0.89,none,none,none,
abridged,abridged,0.18,Adjective,8,6,2,abridged,12.56,0.89,12.56,0.89,none,none,none,
abridgement,abridgement,0.02,Noun,11,9,3,abridgement,12.32,0.85,12.32,0.85,none,12.58,none,
abridging,abridging,0.04,Verb,9,7,3,abridge,none,none,14.35,0.89,none,none,none,
abroad,abroad,4.90,Adverb,6,5,2,abroad,10.15,1.00,10.15,1.00,6.88,none,none,10.22
abrogate,abrogate,0.02,Verb,8,7,3,abrogate,15.22,0.47,15.22,0.47,none,none,none,
abrogated,abrogated,0.04,Verb,9,9,4,abrogate,none,none,15.22,0.47,none,none,none,
abrupt,abrupt,1.14,Adjective,6,6,2,abrupt,10.95,1.00,10.95,1.00,none,10.94,none,10.51
abruption,abruption,0.12,Noun,9,8,3,abruption,11.82,0.94,11.82,0.94,none,none,none,
abruptness,abruptness,0.08,Noun,10,9,3,abruptness,12.39,1.00,12.39,1.00,none,none,none,
abscess,abscess,0.49,Noun,7,5,2,abscess,13.11,0.90,13.11,0.90,none,10.49,none,
abscesses,abscesses,0.14,Noun,9,7,3,abscess,none,none,13.11,0.90,none,10.49,none,
abscond,abscond,0.10,Verb,7,7,2,abscond,13.36,0.74,13.36,0.74,none,none,none,
absconded,absconded,0.10,Verb,9,9,3,abscond,none,none,13.36,0.74,none,none,none,
absconder,absconder,0.02,Noun,9,9,3,absconder,16.62,0.68,16.62,0.68,none,none,none,
absconders,absconders,0.04,Verb,10,10,3,absconder,none,none,16.62,0.68,none,none,none,
absence,absence,6.31,Noun,7,6,2,absence,7.70,1.00,7.70,1.00,6.88,none,none,8.62
absences,absences,0.27,Noun,8,8,3,absence,none,none,7.70,1.00,6.88,none,none,8.62
absent,absent,2.57,Adjective,6,6,2,absent,6.50,1.00,6.50,1.00,6.11,none,none,7.34
absentee,absentee,0.35,Noun,8,7,3,absentee,11.00,0.94,11.00,0.94,none,none,none,
absenteeism,absenteeism,0.10,Noun,11,11,5,absenteeism,13.43,0.78,13.43,0.78,none,none,none,
absentees,absentees,0.08,Noun,9,8,3,absentee,none,none,11.00,0.94,none,none,none,
absenting,absenting,0.04,Verb,9,8,3,absent,none,none,6.50,1.00,6.11,none,none,7.34
absentminded,absentminded,0.06,Adjective,12,12,4,absentminded,10.84,1.00,10.84,1.00,none,none,none,
absentmindedness,absentmindedness,0.02,Noun,16,15,5,absentmindedness,9.61,1.00,9.61,1.00,none,none,none,
absinthe,absinthe,0.55,Verb,8,6,2,absinthe,16.29,0.89,16.29,0.89,none,none,none,
absinthes,absinthes,0.06,Noun,9,7,2,absinthe,none,none,16.29,0.89,none,none,none,
absolute,absolute,11.31,Adjective,8,7,3,absolute,8.53,1.00,8.53,1.00,none,none,none,
absolutely,absolutely,113.12,Adverb,10,9,4,absolutely,7.08,1.00,7.08,1.00,8.02,none,none,
absoluteness,absoluteness,0.02,Noun,12,10,4,absoluteness,12.68,0.95,12.68,0.95,none,none,none,
absolutes,absolutes,0.41,Noun,9,8,3,absolute,none,none,8.53,1.00,none,none,none,
absolution,absolution,0.94,Noun,10,9,4,absolution,11.71,0.94,11.71,0.94,none,11.90,none,
absolve,absolve,0.57,Verb,7,6,2,absolve,12.90,1.00,12.90,1.00,11.78,none,none,11.09
absolved,absolved,0.29,Verb,8,7,2,absolve,none,none,12.90,1.00,11.78,none,none,11.09
absolver,absolver,0.08,Noun,8,8,3,absolver,13.60,0.83,13.60,0.83,none,none,none,
absolves,absolves,0.08,Verb,8,7,2,absolve,none,none,12.90,1.00,11.78,none,none,11.09
absolving,absolving,0.10,Verb,9,8,3,absolve,none,none,12.90,1.00,11.78,none,none,11.09
absorb,absorb,1.98,Verb,6,6,2,absorb,8.83,1.00,8.83,1.00,none,8.69,none,9.49
absorbed,absorbed,1.53,Verb,8,7,2,absorb,none,none,8.83,1.00,none,8.69,none,9.49
absorbent,absorbent,0.16,Adjective,9,9,3,absorbent,10.32,1.00,10.32,1.00,none,none,none,
absorber,absorber,0.06,Noun,8,7,3,absorber,10.33,1.00,10.33,1.00,none,none,none,
absorbers,absorbers,0.16,Noun,9,8,3,absorber,none,none,10.33,1.00,none,none,none,
absorbing,absorbing,0.45,Verb,9,8,3,absorb,none,none,8.83,1.00,none,8.69,none,9.49
absorbs,absorbs,0.49,Verb,7,7,2,absorb,none,none,8.83,1.00,none,8.69,none,9.49
absorption,absorption,0.35,Noun,10,9,3,absorption,10.94,0.95,10.94,0.95,none,none,none,
absorptions,absorptions,0.02,Noun,11,10,3,absorption,none,none,10.94,0.95,none,none,none,
abstain,abstain,0.51,Verb,7,6,2,abstain,12.95,1.00,12.95,1.00,11.36,none,none,
abstained,abstained,0.08,Verb,9,7,2,abstain,none,none,12.95,1.00,11.36,none,none,
abstaining,abstaining,0.16,Verb,10,8,3,abstain,none,none,12.95,1.00,11.36,none,none,
abstains,abstains,0.04,Verb,8,7,2,abstain,none,none,12.95,1.00,11.36,none,none,
abstention,abstention,0.12,Noun,10,9,3,abstention,15.19,0.76,15.19,0.76,none,none,none,
abstentions,abstentions,0.02,Noun,11,10,3,abstention,none,none,15.19,0.76,none,none,none,
abstinence,abstinence,0.88,Noun,10,9,3,abstinence,12.74,1.00,12.74,1.00,none,none,none,
abstract,abstract,2.18,Adjective,8,8,2,abstract,11.47,1.00,11.47,1.00,none,none,none,10.51
abstracting,abstracting,0.02,Verb,11,10,3,abstract,none,none,11.47,1.00,none,none,none,10.51
abstraction,abstraction,0.29,Noun,11,10,3,abstraction,13.65,0.94,13.65,0.94,none,none,none,
abstractionist,abstractionist,0.02,Noun,14,13,4,abstractionist,14.72,0.95,14.72,0.95,none,none,none,
abstractions,abstractions,0.10,Noun,12,11,3,abstraction,none,none,13.65,0.94,none,none,none,
abstracts,abstracts,0.14,Noun,9,9,2,abstract,none,none,11.47,1.00,none,none,none,10.51
abstruse,abstruse,0.06,Adjective,8,7,2,abstruse,14.60,0.50,14.60,0.50,none,none,none,
absurd,absurd,9.71,Adjective,6,5,2,absurd,10.50,0.99,10.50,0.99,none,10.36,none,10.60
absurdist,absurdist,0.02,Noun,9,8,3,absurdist,13.00,0.57,13.00,0.57,none,none,none,
absurdities,absurdities,0.12,Noun,11,9,4,absurdity,none,none,13.21,1.00,none,none,none,
absurdity,absurdity,0.63,Noun,9,8,4,absurdity,13.21,1.00,13.21,1.00,none,none,none,
abundance,abundance,0.88,Noun,9,8,3,abundance,10.45,1.00,10.45,1.00,none,10.22,none,
abundant,abundant,0.57,Adjective,8,8,3,abundant,12.84,1.00,12.84,1.00,none,none,none,
abuse,abuse,10.25,Noun,5,5,2,abuse,8.61,1.00,8.61,1.00,10.68,none,none,8.20
abused,abused,4.53,Verb,6,6,2,abuse,none,none,8.61,1.00,10.68,none,none,8.20
abuser,abuser,0.31,Noun,6,6,3,abuser,9.94,1.00,9.94,1.00,none,none,none,
abusers,abusers,0.22,Noun,7,7,3,abuser,none,none,9.94,1.00,none,none,none,
abuses,abuses,0.49,Noun,6,7,3,abuse,none,none,8.61,1.00,10.68,none,none,8.20
abusing,abusing,1.20,Verb,7,7,3,abuse,none,none,8.61,1.00,10.68,none,none,8.20
abusive,abusive,1.63,Adjective,7,7,3,abusive,9.58,1.00,9.58,1.00,none,none,none,
abusiveness,abusiveness,0.02,Noun,11,10,4,abusiveness,10.47,1.00,10.47,1.00,none,none,none,
abut,abut,0.12,Verb,4,4,2,abut,13.11,0.43,13.11,0.43,none,none,none,
abutment,abutment,0.12,Noun,8,8,3,abutment,15.33,0.50,15.33,0.50,none,none,none,
abutments,abutments,0.02,Noun,9,9,3,abutment,none,none,15.33,0.50,none,none,none,
abuts,abuts,0.04,Verb,5,5,2,abut,none,none,13.11,0.43,none,none,none,
abuzz,abuzz,0.04,Noun,5,4,2,abuzz,13.00,0.78,13.00,0.78,none,none,none,
abysm,abysm,Err:512,Err:512,5,6,3,abysm,15.25,0.19,15.25,0.19,none,none,none,
abysmal,abysmal,0.41,Adjective,7,7,3,abysmal,13.78,0.95,13.78,0.95,none,none,none,
abyss,abyss,1.76,Noun,5,4,2,abyss,11.00,1.00,11.00,1.00,none,11.70,none,12.22
abyssal,abyssal,0.02,Noun,7,6,3,abyssal,17.27,0.58,17.27,0.58,none,none,none,
abysses,abysses,0.04,Noun,7,6,3,abyss,none,none,11.00,1.00,none,11.70,none,12.22
ac,ac,2.16,Abbreviation,2,3,2,ac,10.75,0.42,10.75,0.42,none,none,none,
acacia,acacia,0.16,Noun,6,5,3,acacia,14.25,0.57,14.25,0.57,none,none,none,
academe,academe,0.04,Noun,7,6,3,academe,13.27,0.75,13.27,0.75,none,none,none,
academes,academes,0.02,Noun,8,7,3,academe,none,none,13.27,0.75,none,none,none,
academia,academia,0.24,Noun,8,8,5,academia,13.74,0.95,13.74,0.95,none,none,none,
academic,academic,4.67,Adjective,8,8,4,academic,9.74,1.00,9.74,1.00,none,none,none,
academician,academician,0.02,Noun,11,10,5,academician,15.21,0.74,15.21,0.74,none,none,none,
academicism,academicism,Err:512,Err:512,11,12,6,academicism,15.00,0.56,15.00,0.56,none,none,none,
academics,academics,0.69,Noun,9,9,4,academic,none,none,9.74,1.00,none,none,none,
academies,academies,0.14,Noun,9,8,4,academy,none,none,9.05,1.00,none,none,none,
academy,academy,12.41,Noun,7,7,4,academy,9.05,1.00,9.05,1.00,none,none,none,
acanthus,acanthus,0.02,Adjective,8,7,3,acanthus,15.17,0.33,15.17,0.33,none,none,none,
accede,accede,0.14,Verb,6,5,2,accede,14.31,0.72,14.31,0.72,none,none,none,
accelerando,accelerando,Err:512,Err:512,11,11,5,accelerando,14.50,0.20,14.50,0.20,none,none,none,
accelerant,accelerant,0.55,Noun,10,9,4,accelerant,12.84,0.95,12.84,0.95,none,none,none,
accelerate,accelerate,1.27,Verb,10,8,4,accelerate,10.74,1.00,10.74,1.00,none,none,none,
accelerated,accelerated,0.96,Adjective,11,10,5,accelerated,10.11,1.00,10.11,1.00,none,none,none,
accelerates,accelerates,0.10,Verb,11,9,4,accelerate,none,none,10.74,1.00,none,none,none,
accelerating,accelerating,0.88,Verb,12,10,5,accelerate,none,none,10.74,1.00,none,none,none,
acceleration,acceleration,0.86,Noun,12,10,5,acceleration,9.84,1.00,9.84,1.00,none,10.54,none,
accelerator,accelerator,1.35,Noun,11,9,5,accelerator,10.27,1.00,10.27,1.00,none,none,none,
accelerators,accelerators,0.08,Noun,12,10,5,accelerator,none,none,10.27,1.00,none,none,none,
accent,accent,12.18,Noun,6,6,2,accent,8.60,0.95,8.60,0.95,none,7.09,none,9.31
accented,accented,0.02,Verb,8,8,3,accent,none,none,8.60,0.95,none,7.09,none,9.31
accents,accents,1.14,Noun,7,7,2,accent,none,none,8.60,0.95,none,7.09,none,9.31
accentuate,accentuate,0.35,Verb,10,9,4,accentuate,13.24,0.85,13.24,0.85,none,none,none,
accentuates,accentuates,0.08,Verb,11,10,4,accentuate,none,none,13.24,0.85,none,none,none,
accentuating,accentuating,0.02,Verb,12,12,5,accentuate,none,none,13.24,0.85,none,none,none,
accept,accept,52.78,Verb,6,6,2,accept,6.74,1.00,6.74,1.00,7.12,none,none,7.65
acceptable,acceptable,5.27,Adjective,10,10,4,acceptable,9.84,1.00,9.84,1.00,none,none,none,
acceptance,acceptance,3.16,Noun,10,9,3,acceptance,8.56,1.00,8.56,1.00,none,none,none,
acceptances,acceptances,0.04,Noun,11,11,4,acceptance,none,none,8.56,1.00,none,none,none,
acceptant,acceptant,0.02,Noun,9,9,3,acceptant,11.89,0.95,11.89,0.95,none,none,none,
accepted,accepted,15.71,Verb,8,8,3,accept,none,none,6.74,1.00,7.12,none,none,7.65
accepting,accepting,4.04,Verb,9,8,3,accept,none,none,6.74,1.00,7.12,none,none,7.65
accepts,accepts,2.08,Verb,7,7,2,accept,none,none,6.74,1.00,7.12,none,none,7.65
access,access,31.67,Noun,6,5,2,access,9.10,1.00,9.10,1.00,none,9.24,none,9.49
accessed,accessed,1.53,Verb,8,6,2,access,none,none,9.10,1.00,none,9.24,none,9.49
accesses,accesses,0.04,Verb,8,7,3,access,none,none,9.10,1.00,none,9.24,none,9.49
accessibility,accessibility,0.04,Noun,13,12,6,accessibility,11.05,1.00,11.05,1.00,none,none,none,
accessible,accessible,1.10,Adjective,10,9,4,accessible,9.94,1.00,9.94,1.00,none,none,none,
accessing,accessing,1.04,Verb,9,7,3,access,none,none,9.10,1.00,none,9.24,none,9.49
accession,accession,0.12,Noun,9,7,3,accession,12.25,0.80,12.25,0.80,none,none,none,
accessories,accessories,1.49,Noun,11,8,4,accessory,none,none,11.15,1.00,none,none,none,
accessorize,accessorise,0.35,Verb,11,8,4,accessorize,11.39,1.00,11.39,1.00,none,none,none,
accessorized,accessorised,0.04,Verb,12,9,4,accessorize,none,none,11.39,1.00,none,none,none,
accessorizing,accessorising,0.12,Verb,13,10,5,accessorize,none,none,11.39,1.00,none,none,none,
accessory,accessory,2.88,Noun,9,7,4,accessory,11.15,1.00,11.15,1.00,none,none,none,
accident,accident,81.29,Noun,8,8,3,accident,5.30,1.00,5.30,1.00,none,6.64,none,
accidental,accidental,3.00,Adjective,10,10,4,accidental,6.75,1.00,6.75,1.00,none,none,none,
accidentals,accidentals,0.02,Noun,11,11,4,accidental,none,none,6.75,1.00,none,none,none,
accidents,accidents,6.67,Noun,9,9,3,accident,none,none,5.30,1.00,none,6.64,none,
acclaim,acclaim,0.43,Noun,7,5,2,acclaim,12.11,1.00,12.11,1.00,none,none,none,
acclaims,acclaims,0.04,Verb,8,6,2,acclaim,none,none,12.11,1.00,none,none,none,
acclamation,acclamation,0.02,Noun,11,9,4,acclamation,14.00,1.00,14.00,1.00,none,none,none,
acclimate,acclimate,0.16,Verb,9,7,3,acclimate,14.24,0.85,14.24,0.85,none,none,none,
acclimates,acclimates,0.02,Verb,10,8,3,acclimate,none,none,14.24,0.85,none,none,none,
acclimating,acclimating,0.06,Verb,11,9,4,acclimate,none,none,14.24,0.85,none,none,none,
acclimatization,acclimatisation,0.02,Noun,15,13,6,acclimatization,16.12,0.80,16.12,0.80,none,none,none,
acclimatize,acclimatise,0.02,Verb,11,9,4,acclimatize,14.00,0.80,14.00,0.80,none,none,none,
accolade,accolade,0.02,Noun,8,6,3,accolade,12.43,0.70,12.43,0.70,none,none,none,
accolades,accolades,0.18,Noun,9,7,3,accolade,none,none,12.43,0.70,none,none,none,
accommodate,accommodate,2.14,Verb,11,8,4,accommodate,9.90,1.00,9.90,1.00,none,none,none,
accommodated,accommodated,0.10,Verb,12,10,5,accommodate,none,none,9.90,1.00,none,none,none,
accommodates,accommodates,0.06,Verb,12,9,4,accommodate,none,none,9.90,1.00,none,none,none,
accommodating,accommodating,0.71,Verb,13,10,5,accommodate,none,none,9.90,1.00,none,none,none,
accommodation,accommodation,0.94,Noun,13,10,5,accommodation,9.58,1.00,9.58,1.00,none,10.54,none,
accommodations,accommodations,1.67,Noun,14,11,5,accommodation,none,none,9.58,1.00,none,10.54,none,
accommodative,accommodative,0.02,Adjective,13,10,5,accommodative,15.18,1.00,15.18,1.00,none,none,none,
accompanied,accompanied,2.06,Verb,11,9,4,accompany,none,none,10.75,1.00,none,none,none,
accompanies,accompanies,0.25,Verb,11,9,4,accompany,none,none,10.75,1.00,none,none,none,
accompaniment,accompaniment,0.29,Noun,13,11,4,accompaniment,11.18,0.94,11.18,0.94,none,none,none,
accompanist,accompanist,0.20,Noun,11,10,4,accompanist,12.78,1.00,12.78,1.00,none,none,none,
accompanists,accompanists,0.02,Noun,12,11,4,accompanist,none,none,12.78,1.00,none,none,none,
accompany,accompany,4.75,Verb,9,8,4,accompany,10.75,1.00,10.75,1.00,none,none,none,
accompanying,accompanying,0.92,Verb,12,10,5,accompany,none,none,10.75,1.00,none,none,none,
accomplice,accomplice,4.24,Noun,10,8,3,accomplice,9.78,1.00,9.78,1.00,none,none,none,
accomplices,accomplices,1.14,Noun,11,10,4,accomplice,none,none,9.78,1.00,none,none,none,
accomplish,accomplish,5.84,Verb,10,8,3,accomplish,7.84,1.00,7.84,1.00,none,none,none,
accomplished,accomplished,7.53,Verb,12,9,3,accomplish,none,none,7.84,1.00,none,none,none,
accomplishes,accomplishes,0.14,Verb,12,10,4,accomplish,none,none,7.84,1.00,none,none,none,
accomplishing,accomplishing,0.51,Verb,13,10,4,accomplish,none,none,7.84,1.00,none,none,none,
accomplishment,accomplishment,1.88,Noun,14,12,4,accomplishment,9.00,1.00,9.00,1.00,none,none,none,
accomplishments,accomplishments,1.59,Noun,15,13,4,accomplishment,none,none,9.00,1.00,none,none,none,
accord,accord,1.63,Noun,6,5,2,accord,9.65,0.94,9.65,0.94,11.93,none,none,10.22
accordance,accordance,1.59,Preposition,10,8,3,accordance,11.75,1.00,11.75,1.00,none,10.70,none,
accorded,accorded,0.14,Verb,8,7,3,accord,none,none,9.65,0.94,11.93,none,none,10.22
according,according,41.86,Preposition,9,7,3,according,8.45,1.00,8.45,1.00,9.14,none,none,
accordion,accordion,1.31,Noun,9,8,4,accordion,8.61,1.00,8.61,1.00,none,none,none,
accordions,accordions,0.06,Noun,10,9,4,accordion,none,none,8.61,1.00,none,none,none,
accords,accords,0.18,Noun,7,6,2,accord,none,none,9.65,0.94,11.93,none,none,10.22
accost,accost,0.06,Verb,6,5,2,accost,13.37,0.86,13.37,0.86,none,none,none,
accosted,accosted,0.67,Verb,8,7,3,accost,none,none,13.37,0.86,none,none,none,
accosts,accosts,0.02,Verb,7,6,2,accost,none,none,13.37,0.86,none,none,none,
account,account,44.71,Noun,7,5,2,account,8.26,1.00,8.26,1.00,none,none,none,9.76
accountability,accountability,0.47,Noun,14,12,6,accountability,12.16,1.00,12.16,1.00,none,none,none,
accountable,accountable,1.41,Adjective,11,9,4,accountable,11.70,1.00,11.70,1.00,none,none,none,
accountancy,accountancy,0.06,Noun,11,9,4,accountancy,14.80,0.79,14.80,0.79,none,none,none,
accountant,accountant,6.18,Noun,10,8,3,accountant,11.67,1.00,11.67,1.00,10.00,none,none,
accountants,accountants,1.08,Noun,11,9,3,accountant,none,none,11.67,1.00,10.00,none,none,
accounted,accounted,3.02,Verb,9,7,3,account,none,none,8.26,1.00,none,none,none,9.76
accounting,accounting,4.51,Noun,10,7,3,accounting,12.05,1.00,12.05,1.00,none,none,none,
accounts,accounts,10.47,Noun,8,6,2,account,none,none,8.26,1.00,none,none,none,9.76
accoutrement,accoutrement,0.12,Noun,12,9,4,accoutrement,13.87,0.83,13.87,0.83,none,none,none,
accoutrements,accoutrements,0.12,Noun,13,10,4,accoutrement,none,none,13.87,0.83,none,none,none,
accreditation,accreditation,0.18,Noun,13,11,5,accreditation,15.53,1.00,15.53,1.00,none,none,none,
accredited,accredited,0.31,Adjective,10,9,4,accredited,13.89,1.00,13.89,1.00,none,none,none,
accrete,accrete,0.02,Verb,7,5,2,accrete,14.00,0.53,14.00,0.53,none,none,none,
accrual,accrual,0.04,Noun,7,6,3,accrual,15.00,0.89,15.00,0.89,none,none,none,
accrue,accrue,0.08,Verb,6,4,2,accrue,14.11,1.00,14.11,1.00,none,none,none,
accrues,accrues,0.02,Verb,7,5,2,accrue,none,none,14.11,1.00,none,none,none,
accruing,accruing,0.04,Verb,8,6,3,accrue,none,none,14.11,1.00,none,none,none,
accumulate,accumulate,0.57,Verb,10,10,4,accumulate,12.30,1.00,12.30,1.00,none,none,none,
accumulated,accumulated,0.76,Verb,11,12,5,accumulate,none,none,12.30,1.00,none,none,none,
accumulates,accumulates,0.22,Verb,11,11,4,accumulate,none,none,12.30,1.00,none,none,none,
accumulating,accumulating,0.35,Verb,12,12,5,accumulate,none,none,12.30,1.00,none,none,none,
accumulation,accumulation,0.25,Noun,12,12,5,accumulation,10.50,1.00,10.50,1.00,none,10.60,none,
accumulations,accumulations,0.02,Noun,13,13,5,accumulation,none,none,10.50,1.00,none,10.60,none,
accumulative,accumulative,0.06,Adjective,12,12,5,accumulative,12.50,1.00,12.50,1.00,none,none,none,
accumulator,accumulator,0.02,Noun,11,11,5,accumulator,13.94,0.84,13.94,0.84,none,none,none,
accuracy,accuracy,1.49,Noun,8,7,4,accuracy,9.71,1.00,9.71,1.00,none,none,none,
accurate,accurate,7.84,Adjective,8,6,3,accurate,9.44,1.00,9.44,1.00,none,none,none,
accursed,accursed,0.65,Adjective,8,7,2,accursed,12.71,0.78,12.71,0.78,none,none,none,
accusation,accusation,2.80,Noun,10,9,4,accusation,10.17,1.00,10.17,1.00,none,none,none,
accusations,accusations,3.00,Noun,11,10,4,accusation,none,none,10.17,1.00,none,none,none,
accusative,accusative,Err:512,Err:512,10,9,4,accusative,13.33,0.79,13.33,0.79,none,none,none,
accusatory,accusatory,0.06,Adjective,10,10,5,accusatory,11.18,1.00,11.18,1.00,none,none,none,
accuse,accuse,5.69,Verb,6,5,2,accuse,9.10,1.00,9.10,1.00,7.72,8.73,none,8.12
accused,accused,13.06,Verb,7,6,2,accuse,none,none,9.10,1.00,7.72,8.73,none,8.12
accuser,accuser,0.29,Noun,7,6,3,accuser,11.72,1.00,11.72,1.00,none,10.07,none,
accusers,accusers,0.16,Noun,8,7,3,accuser,none,none,11.72,1.00,none,10.07,none,
accuses,accuses,0.61,Verb,7,7,3,accuse,none,none,9.10,1.00,7.72,8.73,none,8.12
accusing,accusing,4.94,Verb,8,7,3,accuse,none,none,9.10,1.00,7.72,8.73,none,8.12
accustom,accustom,0.12,Verb,8,7,3,accustom,12.00,0.94,12.00,0.94,none,none,none,
ace,ace,14.73,Noun,3,2,1,ace,8.83,1.00,8.83,1.00,none,none,8.12,
aced,aced,0.76,Verb,4,3,1,ace,none,none,8.83,1.00,none,none,8.12,
acerb,acerb,Err:512,Err:512,5,5,2,acerb,15.67,0.14,15.67,0.14,none,none,none,
acerbic,acerbic,0.08,Adjective,7,7,3,acerbic,16.62,0.68,16.62,0.68,none,none,none,
acerbity,acerbity,Err:512,Err:512,8,8,5,acerbity,15.75,0.40,15.75,0.40,none,none,none,
aces,aces,3.96,Noun,4,4,2,ace,none,none,8.83,1.00,none,none,8.12,
acetaminophen,acetaminophen,0.10,Noun,13,12,6,acetaminophen,13.47,0.94,13.47,0.94,none,none,none,
acetate,acetate,0.12,Noun,7,6,3,acetate,12.85,0.68,12.85,0.68,none,none,none,
acetates,acetates,0.02,Noun,8,7,3,acetate,none,none,12.85,0.68,none,none,none,
acetic,acetic,0.02,Adjective,6,6,3,acetic,13.75,0.84,13.75,0.84,none,none,none,
acetone,acetone,0.16,Noun,7,6,3,acetone,14.05,0.95,14.05,0.95,none,none,none,
acetyl,acetyl,0.04,Noun,6,6,3,acetyl,16.31,0.76,16.31,0.76,none,none,none,
acetylcholine,acetylcholine,0.14,Verb,13,11,5,acetylcholine,16.30,0.56,16.30,0.56,none,none,none,
acetylene,acetylene,0.45,Noun,9,8,4,acetylene,16.88,0.85,16.88,0.85,none,none,none,
acetylsalicylic,acetylsalicylic,0.08,Adjective,15,15,7,acetylsalicylic,17.71,0.39,17.71,0.39,none,none,none,
ache,ache,2.49,Noun,4,2,1,ache,5.79,1.00,5.79,1.00,none,6.13,7.49,
ached,ached,0.31,Verb,5,3,1,ache,none,none,5.79,1.00,none,6.13,7.49,
aches,aches,1.51,Verb,5,3,1,ache,none,none,5.79,1.00,none,6.13,7.49,
achievable,achievable,0.04,Adjective,10,8,4,achievable,9.89,1.00,9.89,1.00,none,none,none,
achieve,achieve,7.33,Verb,7,4,2,achieve,8.53,1.00,8.53,1.00,8.11,none,none,8.53
achieved,achieved,4.29,Verb,8,5,2,achieve,none,none,8.53,1.00,8.11,none,none,8.53
achievement,achievement,3.49,Noun,11,8,3,achievement,8.80,1.00,8.80,1.00,8.29,none,none,
achievements,achievements,1.08,Noun,12,9,3,achievement,none,none,8.80,1.00,8.29,none,none,
achiever,achiever,0.24,Verb,8,5,3,achiever,9.11,1.00,9.11,1.00,none,none,none,
achievers,achievers,0.18,Noun,9,6,3,achiever,none,none,9.11,1.00,none,none,none,
achieves,achieves,0.31,Verb,8,5,2,achieve,none,none,8.53,1.00,8.11,none,none,8.53
achieving,achieving,0.73,Verb,9,6,3,achieve,none,none,8.53,1.00,8.11,none,none,8.53
achiness,achiness,0.02,Noun,8,6,3,achiness,10.11,0.95,10.11,0.95,none,none,none,
achy,achy,0.45,Adjective,4,3,2,achy,8.06,0.95,8.06,0.95,none,none,none,
acid,acid,9.96,Noun,4,4,2,acid,9.60,1.00,9.60,1.00,none,none,none,8.96
acidic,acidic,0.22,Adjective,6,6,3,acidic,11.21,1.00,11.21,1.00,none,none,none,
acidity,acidity,0.20,Noun,7,7,4,acidity,12.78,1.00,12.78,1.00,none,12.17,none,
acidophilus,acidophilus,0.06,Noun,11,10,5,acidophilus,17.40,0.56,17.40,0.56,none,none,none,
acidosis,acidosis,0.16,Noun,8,8,4,acidosis,16.85,0.65,16.85,0.65,none,none,none,
acids,acids,0.82,Noun,5,5,2,acid,none,none,9.60,1.00,none,none,none,8.96
acidulous,acidulous,Err:512,Err:512,9,8,4,acidulous,15.43,0.37,15.43,0.37,none,none,none,
acing,acing,0.08,Verb,5,4,2,ace,none,none,8.83,1.00,none,none,8.12,
acknowledge,acknowledge,6.14,Verb,11,7,3,acknowledge,9.11,1.00,9.11,1.00,9.27,none,none,
acknowledged,acknowledged,1.96,Verb,12,8,3,acknowledge,none,none,9.11,1.00,9.27,none,none,
acknowledgement,acknowledgement,0.41,Noun,15,11,4,acknowledgement,9.95,1.00,9.95,1.00,none,none,none,
acknowledgements,acknowledgements,0.08,Noun,16,12,4,acknowledgement,none,none,9.95,1.00,none,none,none,
acknowledges,acknowledges,0.25,Verb,12,9,4,acknowledge,none,none,9.11,1.00,9.27,none,none,
acknowledging,acknowledging,0.71,Verb,13,9,4,acknowledge,none,none,9.11,1.00,9.27,none,none,
acknowledgment,acknowledgement,0.51,Noun,14,11,4,acknowledgement,none,none,9.95,1.00,none,none,none,
acknowledgments,acknowledgements,0.02,Noun,15,12,4,acknowledgement,none,none,9.95,1.00,none,none,none,
acme,acme,1.25,Noun,4,4,2,acme,9.54,0.65,9.54,0.65,none,none,none,
acne,acne,0.90,Noun,4,4,2,acne,11.40,1.00,11.40,1.00,none,none,none,9.58
acnes,acnes,0.02,Noun,5,4,1,acne,none,none,11.40,1.00,none,none,none,9.58
acolyte,acolyte,0.14,Noun,7,6,3,acolyte,13.77,0.68,13.77,0.68,none,none,none,
acolytes,acolytes,0.12,Noun,8,7,3,acolyte,none,none,13.77,0.68,none,none,none,
aconitum,aconitum,0.08,Noun,8,8,4,aconitum,11.00,0.10,11.00,0.10,none,none,none,
acorn,acorn,0.73,Noun,5,5,2,acorn,5.95,0.95,5.95,0.95,none,none,none,7.04
acorns,acorns,0.37,Noun,6,6,2,acorn,none,none,5.95,0.95,none,none,none,7.04
acoustic,acoustic,0.96,Adjective,8,7,3,acoustic,12.39,1.00,12.39,1.00,none,none,none,
acoustical,acoustical,0.12,Adjective,10,9,4,acoustical,12.90,0.95,12.90,0.95,none,none,none,
acoustics,acoustics,0.43,Noun,9,8,3,acoustics,11.32,1.00,11.32,1.00,none,none,none,
acquaint,acquaint,0.39,Verb,8,6,2,achiever,none,none,9.11,1.00,none,none,none,
acquaintance,acquaintance,5.35,Noun,12,9,3,acquaintance,10.48,1.00,10.48,1.00,none,none,none,
acquaintances,acquaintances,1.06,Noun,13,11,4,acquaintance,none,none,10.48,1.00,none,none,none,
acquaintanceship,acquaintanceship,0.08,Noun,16,12,4,acquaintanceship,14.18,0.89,14.18,0.89,none,none,none,
acquiesce,acquiesce,0.06,Verb,9,6,3,acquiesce,14.25,0.84,14.25,0.84,none,none,none,
acquiesced,acquiesced,0.10,Verb,10,7,3,acquiesce,none,none,14.25,0.84,none,none,none,
acquiescence,acquiescence,0.10,Noun,12,9,4,acquiescence,13.61,0.90,13.61,0.90,none,none,none,
acquiescent,acquiescent,0.02,Adjective,11,9,4,acquiescent,14.18,0.61,14.18,0.61,none,none,none,
acquiescing,acquiescing,0.04,Verb,11,8,4,acquiesce,none,none,14.25,0.84,none,none,none,
acquire,acquire,2.65,Verb,7,5,3,acquire,9.94,1.00,9.94,1.00,none,none,none,10.04
acquired,acquired,4.47,Verb,8,6,3,acquire,none,none,9.94,1.00,none,none,none,10.04
acquirer,acquirer,0.02,Noun,8,6,4,acquirer,11.79,1.00,11.79,1.00,none,none,none,
acquires,acquires,0.16,Verb,8,6,3,acquire,none,none,9.94,1.00,none,none,none,10.04
acquiring,acquiring,0.84,Verb,9,7,3,acquire,none,none,9.94,1.00,none,none,none,10.04
acquisition,acquisition,1.45,Noun,11,9,4,acquisition,13.63,1.00,13.63,1.00,none,none,none,
acquisitions,acquisitions,0.65,Noun,12,10,4,acquisition,none,none,13.63,1.00,none,none,none,
acquisitive,acquisitive,0.06,Adjective,11,9,4,acquisitive,14.19,0.80,14.19,0.80,none,none,none,
acquisitiveness,acquisitiveness,0.02,Noun,15,12,5,acquisitiveness,14.38,0.84,14.38,0.84,none,none,none,
acquit,acquit,0.47,Noun,6,5,2,acquit,13.22,1.00,13.22,1.00,none,none,none,
acquittal,acquittal,0.73,Noun,9,7,3,acquittal,12.39,1.00,12.39,1.00,none,none,none,
acquittals,acquittals,0.02,Noun,10,8,3,acquittal,none,none,12.39,1.00,none,none,none,
acquitted,acquitted,1.53,Verb,9,7,3,acquit,none,none,13.22,1.00,none,none,none,
acquitting,acquitting,0.12,Verb,10,7,3,acquit,none,none,13.22,1.00,none,none,none,
acre,acre,1.82,Noun,4,3,2,acre,9.80,1.00,9.80,1.00,none,8.18,none,8.62
acreage,acreage,0.43,Noun,7,5,3,acreage,13.13,0.79,13.13,0.79,none,none,none,
acres,acres,4.59,Noun,5,4,2,acre,none,none,9.80,1.00,none,8.18,none,8.62
acrid,acrid,0.04,Adjective,5,5,2,acrid,14.07,0.79,14.07,0.79,none,none,none,
acrimonious,acrimonious,0.14,Adjective,11,10,5,acrimonious,16.30,0.56,16.30,0.56,none,none,none,
acrimony,acrimony,0.22,Noun,8,8,4,acrimony,14.75,0.63,14.75,0.63,none,none,none,
acrobat,acrobat,0.59,Noun,7,7,3,acrobat,8.05,1.00,8.05,1.00,none,none,none,
acrobatic,acrobatic,0.24,Adjective,9,9,4,acrobatic,9.38,1.00,9.38,1.00,none,none,none,
acrobatics,acrobatics,0.22,Noun,10,10,4,acrobatics,9.00,1.00,9.00,1.00,none,none,none,
acrobats,acrobats,0.37,Noun,8,8,3,acrobat,none,none,8.05,1.00,none,none,none,
acronym,acronym,0.29,Noun,7,7,3,acronym,11.56,1.00,11.56,1.00,none,none,none,
acronyms,acronyms,0.14,Noun,8,8,3,acronym,none,none,11.56,1.00,none,none,none,
acrophobia,acrophobia,0.20,Noun,10,9,5,acrophobia,14.38,0.84,14.38,0.84,none,none,none,
acropolis,acropolis,0.12,Noun,9,9,4,acropolis,15.40,0.79,15.40,0.79,none,none,none,
across,across,94.14,Preposition,6,5,2,across,6.64,1.00,6.64,1.00,5.16,none,none,
acrostic,acrostic,Err:512,Err:512,8,8,3,acrostic,14.82,0.58,14.82,0.58,none,none,none,
acrylic,acrylic,0.45,Adjective,7,7,3,acrylic,11.56,0.95,11.56,0.95,none,none,none,
act,act,109.12,Verb,3,3,1,act,6.42,1.00,6.42,1.00,5.46,6.45,5.89,
acted,acted,10.75,Verb,5,5,2,act,none,none,6.42,1.00,5.46,6.45,5.89,
acting,acting,52.45,Verb,6,5,2,act,none,none,6.42,1.00,5.46,6.45,5.89,
actinic,actinic,0.02,Adjective,7,7,3,actinic,15.62,0.42,15.62,0.42,none,none,none,
actinium,actinium,Err:512,Err:512,8,8,4,actinium,NA,0.00,NA,0.00,none,none,none,
action,action,61.08,Noun,6,5,2,action,6.67,1.00,6.67,1.00,none,none,none,6.97
actionable,actionable,0.25,Adjective,10,9,4,actionable,12.50,0.95,12.50,0.95,none,none,none,
actions,actions,12.16,Noun,7,6,2,action,none,none,6.67,1.00,none,none,none,6.97
activate,activate,4.67,Verb,8,7,3,activate,11.11,1.00,11.11,1.00,none,none,none,
activated,activated,4.31,Verb,9,9,4,activate,none,none,11.11,1.00,none,none,none,
activates,activates,0.31,Verb,9,8,3,activate,none,none,11.11,1.00,none,none,none,
activating,activating,0.86,Verb,10,9,4,activate,none,none,11.11,1.00,none,none,none,
activation,activation,0.84,Noun,10,9,4,activation,9.78,1.00,9.78,1.00,none,none,none,
activator,activator,0.08,Noun,9,8,4,activator,11.50,1.00,11.50,1.00,none,none,none,
active,active,11.45,Adjective,6,5,2,active,6.53,1.00,6.53,1.00,none,7.71,none,7.19
actives,actives,0.02,Noun,7,6,2,active,none,none,6.53,1.00,none,7.71,none,7.19
activism,activism,0.18,Noun,8,9,4,activism,11.89,1.00,11.89,1.00,none,none,none,
activist,activist,0.98,Noun,8,8,3,activist,11.80,1.00,11.80,1.00,none,none,none,
activists,activists,0.35,Noun,9,9,3,activist,none,none,11.80,1.00,none,none,none,
activities,activities,8.61,Noun,10,9,4,activity,none,none,6.47,1.00,none,none,none,
activity,activity,12.88,Noun,8,8,4,activity,6.47,1.00,6.47,1.00,none,none,none,
actor,actor,26.33,Noun,5,4,2,actor,7.17,1.00,7.17,1.00,none,6.54,none,6.40
actors,actors,12.63,Noun,6,5,2,actor,none,none,7.17,1.00,none,6.54,none,6.40
actress,actress,16.02,Noun,7,6,2,actress,6.17,1.00,6.17,1.00,none,6.22,none,6.82
actresses,actresses,2.25,Noun,9,8,3,actress,none,none,6.17,1.00,none,6.22,none,6.82
acts,acts,12.47,Noun,4,4,1,act,none,none,6.42,1.00,5.46,6.45,5.89,
actual,actual,17.33,Adjective,6,7,3,actual,6.94,1.00,6.94,1.00,none,8.11,none,
actualities,actualities,0.02,Noun,11,11,5,actuality,none,none,12.38,1.00,none,11.49,none,
actuality,actuality,0.47,Noun,9,10,5,actuality,12.38,1.00,12.38,1.00,none,11.49,none,
actualization,actualization,0.06,Noun,13,13,6,actualization,13.84,0.90,13.84,0.90,none,none,none,
actualize,actualize,0.04,Verb,9,9,4,actualize,13.61,1.00,13.61,1.00,none,none,none,
actually,actually,322.33,Adverb,8,8,4,actually,7.33,1.00,7.33,1.00,6.84,none,none,
actuarial,actuarial,0.18,Adjective,9,10,5,actuarial,15.85,0.68,15.85,0.68,none,none,none,
actuaries,actuaries,0.02,Noun,9,9,4,actuary,none,none,17.27,0.79,none,none,none,
actuary,actuary,0.14,Noun,7,8,4,actuary,17.27,0.79,17.27,0.79,none,none,none,
actuation,actuation,0.02,Noun,9,9,4,actuation,14.69,0.72,14.69,0.72,none,none,none,
actuator,actuator,0.04,Noun,8,8,4,actuator,13.71,0.78,13.71,0.78,none,none,none,
actuators,actuators,0.04,Noun,9,9,4,actuator,none,none,13.71,0.78,none,none,none,
acuity,acuity,0.27,Noun,6,7,4,acuity,13.88,0.44,13.88,0.44,none,none,none,
acumen,acumen,0.14,Noun,6,7,3,acumen,14.44,0.80,14.44,0.80,none,none,none,
acupressure,acupressure,0.12,Noun,11,10,4,acupressure,14.44,0.95,14.44,0.95,none,none,none,
acupuncture,acupuncture,0.73,Noun,11,10,4,acupuncture,11.79,1.00,11.79,1.00,none,none,none,
acupuncturist,acupuncturist,0.18,Noun,13,15,5,acupuncturist,14.61,0.95,14.61,0.95,none,none,none,
acupuncturists,acupuncturists,0.04,Noun,14,16,5,acupuncturist,none,none,14.61,0.95,none,none,none,
acute,acute,2.94,Adjective,5,5,2,acute,11.60,1.00,11.60,1.00,none,11.92,none,11.09
acuteness,acuteness,0.02,Noun,9,8,3,acuteness,12.95,0.95,12.95,0.95,none,none,none,
acyclovir,acyclovir,0.02,Noun,9,9,4,acyclovir,12.57,0.37,12.57,0.37,none,none,none,
ad,ad,13.61,Noun,2,2,1,ad,8.22,1.00,8.22,1.00,none,none,7.42,
adage,adage,0.63,Noun,5,4,2,adage,16.21,0.74,16.21,0.74,none,none,none,12.96
adages,adages,0.02,Noun,6,6,3,adage,none,none,16.21,0.74,none,none,none,12.96
adagio,adagio,0.24,Noun,6,6,4,adagio,12.62,0.38,12.62,0.38,none,none,none,
adamant,adamant,0.98,Adjective,7,7,3,adamant,14.29,0.78,14.29,0.78,none,none,none,
adamantine,adamantine,0.02,Adjective,10,9,4,adamantine,15.91,0.55,15.91,0.55,none,none,none,
adapt,adapt,2.63,Verb,5,5,2,adapt,9.00,1.00,9.00,1.00,none,9.49,none,10.51
adaptability,adaptability,0.14,Noun,12,12,6,adaptability,11.65,0.94,11.65,0.94,none,none,none,
adaptable,adaptable,0.33,Adjective,9,9,4,adaptable,9.37,1.00,9.37,1.00,none,none,none,
adaptation,adaptation,1.59,Noun,10,9,4,adaptation,11.39,1.00,11.39,1.00,none,none,none,
adaptations,adaptations,0.06,Noun,11,10,4,adaptation,none,none,11.39,1.00,none,none,none,
adapted,adapted,1.10,Verb,7,7,3,adapt,none,none,9.00,1.00,none,9.49,none,10.51
adapter,adapter,0.22,Noun,7,6,3,adapter,11.79,1.00,11.79,1.00,none,none,none,
adapting,adapting,0.53,Verb,8,7,3,adapt,none,none,9.00,1.00,none,9.49,none,10.51
adaption,adaption,0.02,Noun,8,7,3,adaption,12.40,1.00,12.40,1.00,none,none,none,
adaptive,adaptive,0.22,Adjective,8,7,3,adaptive,11.33,1.00,11.33,1.00,none,none,none,
adaptor,adaptor,0.08,Noun,7,6,3,adaptor,10.21,1.00,10.21,1.00,none,none,none,
adaptors,adaptors,0.02,Noun,8,7,3,adaptor,none,none,10.21,1.00,none,none,none,
adapts,adapts,0.06,Verb,6,6,2,adapt,none,none,9.00,1.00,none,9.49,none,10.51
add,add,33.06,Verb,3,2,1,add,5.10,1.00,5.10,1.00,4.43,none,4.43,
added,added,9.65,Verb,5,4,2,add,none,none,5.10,1.00,4.43,none,4.43,
addenda,addenda,0.04,Noun,7,6,3,addendum,none,none,15.65,0.89,none,none,none,
addendum,addendum,0.29,Noun,8,6,3,addendum,15.65,0.89,15.65,0.89,none,none,none,
addendums,addendums,0.08,Noun,9,7,3,addendum,none,none,15.65,0.89,none,none,none,
adder,adder,1.43,Noun,5,3,2,adder,12.13,0.83,12.13,0.83,none,7.15,none,
adders,adders,0.04,Noun,6,4,2,adder,none,none,12.13,0.83,none,7.15,none,
addict,addict,5.35,Noun,6,5,2,addict,11.17,1.00,11.17,1.00,none,10.47,none,10.41
addicted,addicted,4.53,Verb,8,7,3,addict,none,none,11.17,1.00,none,10.47,none,10.41
addicting,addicting,0.06,Verb,9,7,3,addict,none,none,11.17,1.00,none,10.47,none,10.41
addiction,addiction,3.90,Noun,9,7,3,addiction,12.11,1.00,12.11,1.00,none,none,none,
addictions,addictions,0.41,Noun,10,8,3,addiction,none,none,12.11,1.00,none,none,none,
addictive,addictive,1.12,Adjective,9,7,3,addictive,10.33,1.00,10.33,1.00,none,none,none,
addictiveness,addictiveness,0.04,Noun,13,10,4,addictiveness,11.61,1.00,11.61,1.00,none,none,none,
addicts,addicts,2.08,Noun,7,6,2,addict,none,none,11.17,1.00,none,10.47,none,10.41
adding,adding,4.02,Verb,6,4,2,add,none,none,5.10,1.00,4.43,none,4.43,
addition,addition,7.75,Preposition,8,6,3,addition,5.76,1.00,5.76,1.00,none,6.35,none,
additional,additional,5.88,Adjective,10,8,4,additional,9.00,1.00,9.00,1.00,none,none,none,
additions,additions,0.22,Noun,9,7,3,addition,none,none,5.76,1.00,none,6.35,none,
additive,additive,0.22,Adjective,8,6,3,additive,12.28,1.00,12.28,1.00,none,none,none,
additives,additives,0.20,Noun,9,7,3,additive,none,none,12.28,1.00,none,none,none,
addle,addle,0.16,Verb,5,4,2,addle,13.08,0.72,13.08,0.72,none,none,none,
addled,addled,0.22,Adjective,6,5,2,addled,12.00,0.79,12.00,0.79,none,none,none,
address,address,52.27,Noun,7,5,2,address,5.15,1.00,5.15,1.00,none,6.07,none,5.49
addressed,addressed,3.80,Verb,9,6,2,address,none,none,5.15,1.00,none,6.07,none,5.49
addressee,addressee,0.12,Noun,9,6,3,addressee,9.95,1.00,9.95,1.00,none,none,none,
addresses,addresses,3.18,Noun,9,7,3,address,none,none,5.15,1.00,none,6.07,none,5.49
addressing,addressing,2.10,Verb,10,7,3,address,none,none,5.15,1.00,none,6.07,none,5.49
adds,adds,3.94,Verb,4,3,1,add,none,none,5.10,1.00,4.43,none,4.43,
adduce,adduce,0.02,Verb,6,4,2,adduce,13.00,0.53,13.00,0.53,none,none,none,
adduced,adduced,0.02,Verb,7,5,2,adduce,none,none,13.00,0.53,none,none,none,
adductor,adductor,0.02,Noun,8,7,3,adductor,14.18,0.52,14.18,0.52,none,none,none,
adenoidal,adenoidal,0.02,Adjective,9,8,4,adenoidal,15.33,0.33,15.33,0.33,none,none,none,
adenoma,adenoma,0.02,Noun,7,7,4,adenoma,13.78,0.47,13.78,0.47,none,none,none,
adenosine,adenosine,0.12,Noun,9,8,4,adenosine,16.29,0.39,16.29,0.39,none,none,none,
adept,adept,0.65,Adjective,5,5,2,adept,12.50,0.78,12.50,0.78,none,none,none,12.11
adeptness,adeptness,0.02,Noun,9,8,3,adeptness,14.37,0.95,14.37,0.95,none,none,none,
adepts,adepts,0.04,Noun,6,6,2,adept,none,none,12.50,0.78,none,none,none,12.11
adequacy,adequacy,0.02,Noun,8,8,4,adequacy,12.50,0.95,12.50,0.95,none,none,none,
adequate,adequate,2.59,Adjective,8,7,3,adequate,10.35,1.00,10.35,1.00,none,none,none,
adhere,adhere,0.57,Verb,6,5,2,adhere,10.78,0.95,10.78,0.95,none,none,none,11.90
adhered,adhered,0.12,Verb,7,6,2,adhere,none,none,10.78,0.95,none,none,none,11.90
adherence,adherence,0.18,Noun,9,8,3,adherence,12.22,1.00,12.22,1.00,none,11.81,none,
adherent,adherent,0.06,Noun,8,8,3,adherent,14.17,1.00,14.17,1.00,none,none,none,
adherents,adherents,0.02,Noun,9,9,3,adherent,none,none,14.17,1.00,none,none,none,
adheres,adheres,0.06,Verb,7,6,2,adhere,none,none,10.78,0.95,none,none,none,11.90
adhering,adhering,0.18,Verb,8,7,3,adhere,none,none,10.78,0.95,none,none,none,11.90
adhesion,adhesion,0.12,Noun,8,7,3,adhesion,13.50,1.00,13.50,1.00,none,none,none,
adhesions,adhesions,0.14,Noun,9,8,3,adhesion,none,none,13.50,1.00,none,none,none,
adhesive,adhesive,0.92,Noun,8,7,3,adhesive,9.17,1.00,9.17,1.00,none,none,none,
adhesives,adhesives,0.02,Noun,9,8,3,adhesive,none,none,9.17,1.00,none,none,none,
adiabatic,adiabatic,0.02,Adjective,9,9,5,adiabatic,18.50,0.22,18.50,0.22,none,none,none,
adipose,adipose,0.08,Verb,7,6,3,adipose,15.70,0.53,15.70,0.53,none,none,none,
adiposity,adiposity,0.02,Noun,9,9,5,adiposity,15.86,0.39,15.86,0.39,none,none,none,
adjacent,adjacent,1.33,Adjective,8,7,3,adjacent,11.05,0.95,11.05,0.95,none,none,none,
adjective,adjective,0.49,Noun,9,7,3,adjective,8.60,1.00,8.60,1.00,none,none,none,
adjectives,adjectives,0.49,Noun,10,8,3,adjective,none,none,8.60,1.00,none,none,none,
adjoining,adjoining,1.12,Adjective,9,6,3,adjoining,11.85,1.00,11.85,1.00,none,none,none,
adjourn,adjourn,1.29,Verb,7,4,2,adjourn,12.59,0.94,12.59,0.94,none,none,none,12.85
adjourned,adjourned,4.90,Verb,9,5,2,adjourn,none,none,12.59,0.94,none,none,none,12.85
adjournment,adjournment,0.22,Noun,11,8,3,adjournment,14.26,1.00,14.26,1.00,none,11.87,none,
adjourns,adjourns,0.04,Verb,8,5,2,adjourn,none,none,12.59,0.94,none,none,none,12.85
adjudge,adjudge,0.02,Verb,7,4,2,adjudge,15.20,0.79,15.20,0.79,none,none,none,
adjudged,adjudged,0.06,Verb,8,5,2,adjudge,none,none,15.20,0.79,none,none,none,
adjudicate,adjudicate,0.10,Verb,10,8,4,adjudicate,15.25,0.89,15.25,0.89,none,none,none,
adjudicated,adjudicated,0.12,Verb,11,10,5,adjudicate,none,none,15.25,0.89,none,none,none,
adjudication,adjudication,0.18,Noun,12,10,5,adjudication,16.33,0.63,16.33,0.63,none,none,none,
adjudicator,adjudicator,0.08,Noun,11,9,5,adjudicator,16.47,0.75,16.47,0.75,none,none,none,
adjunct,adjunct,0.14,Noun,7,6,2,adjunct,13.06,0.84,13.06,0.84,none,none,none,
adjuration,adjuration,Err:512,Err:512,10,9,4,adjuration,16.00,0.37,16.00,0.37,none,none,none,
adjure,adjure,0.02,Verb,6,5,2,adjure,13.10,0.56,13.10,0.56,none,none,none,
adjust,adjust,5.63,Verb,6,5,2,adjust,8.43,1.00,8.43,1.00,none,9.77,none,9.76
adjustability,adjustability,0.02,Noun,13,12,6,adjustability,12.05,1.00,12.05,1.00,none,none,none,
adjustable,adjustable,0.31,Adjective,10,9,4,adjustable,8.95,1.00,8.95,1.00,none,none,none,
adjusted,adjusted,1.80,Verb,8,7,3,adjust,none,none,8.43,1.00,none,9.77,none,9.76
adjuster,adjuster,0.39,Noun,8,6,3,adjuster,11.28,1.00,11.28,1.00,none,none,none,
adjusters,adjusters,0.14,Noun,9,7,3,adjuster,none,none,11.28,1.00,none,none,none,
adjusting,adjusting,2.37,Verb,9,7,3,adjust,none,none,8.43,1.00,none,9.77,none,9.76
adjustment,adjustment,3.10,Noun,10,9,3,adjustment,9.74,1.00,9.74,1.00,none,none,none,
adjustments,adjustments,1.39,Noun,11,10,3,adjustment,none,none,9.74,1.00,none,none,none,
adjustor,adjustor,0.08,Noun,8,6,3,adjustor,13.29,0.89,13.29,0.89,none,none,none,
adjusts,adjusts,0.18,Verb,7,6,2,adjust,none,none,8.43,1.00,none,9.77,none,9.76
adjutant,adjutant,0.88,Noun,8,7,3,adjutant,13.91,0.61,13.91,0.61,none,none,none,
adjutants,adjutants,0.02,Noun,9,8,3,adjutant,none,none,13.91,0.61,none,none,none,
adjuvant,adjuvant,0.02,Adjective,8,7,3,adjuvant,14.25,0.63,14.25,0.63,none,none,none,
admin,admin,0.43,Noun,5,5,2,admin,13.89,0.95,13.89,0.95,none,none,none,
administer,administer,1.67,Verb,10,9,4,administer,9.42,1.00,9.42,1.00,none,none,none,
administered,administered,1.49,Verb,12,10,4,administer,none,none,9.42,1.00,none,none,none,
administering,administering,0.47,Verb,13,11,5,administer,none,none,9.42,1.00,none,none,none,
administers,administers,0.12,Verb,11,10,4,administer,none,none,9.42,1.00,none,none,none,
administrate,administrate,0.04,Verb,12,11,4,administrate,12.50,1.00,12.50,1.00,none,none,none,
administrates,administrates,0.02,Verb,13,12,4,administrate,none,none,12.50,1.00,none,none,none,
administration,administration,7.59,Noun,14,13,5,administration,11.11,1.00,11.11,1.00,none,11.02,none,
administrations,administrations,0.31,Noun,15,14,5,administration,none,none,11.11,1.00,none,11.02,none,
administrative,administrative,1.61,Adjective,14,13,5,administrative,11.50,1.00,11.50,1.00,none,none,none,
administrator,administrator,1.65,Noun,13,12,5,administrator,12.22,1.00,12.22,1.00,11.41,none,none,
administrators,administrators,0.35,Noun,14,13,5,administrator,none,none,12.22,1.00,11.41,none,none,
admirable,admirable,2.61,Adjective,9,8,4,admirable,11.42,1.00,11.42,1.00,none,none,none,
admiral,admiral,16.57,Noun,7,6,3,admiral,8.72,1.00,8.72,1.00,none,none,none,
admirals,admirals,0.57,Noun,8,7,3,admiral,none,none,8.72,1.00,none,none,none,
admiralty,admiralty,0.67,Noun,9,8,4,admiralty,11.41,0.89,11.41,0.89,none,none,none,
admiration,admiration,2.10,Noun,10,8,4,admiration,8.95,1.00,8.95,1.00,none,none,none,
admire,admire,14.16,Verb,6,5,2,admire,7.42,1.00,7.42,1.00,7.72,none,none,8.20
admired,admired,3.67,Verb,7,6,3,admire,none,none,7.42,1.00,7.72,none,none,8.20
admirer,admirer,2.96,Noun,7,6,3,admirer,8.78,0.95,8.78,0.95,none,none,none,
admirers,admirers,0.96,Noun,8,7,3,admirer,none,none,8.78,0.95,none,none,none,
admires,admires,0.51,Verb,7,6,2,admire,none,none,7.42,1.00,7.72,none,none,8.20
admiring,admiring,2.76,Verb,8,7,3,admire,none,none,7.42,1.00,7.72,none,none,8.20
admissibility,admissibility,0.08,Noun,13,12,6,admissibility,13.79,0.78,13.79,0.78,none,none,none,
admissible,admissible,0.73,Adjective,10,9,4,admissible,13.10,1.00,13.10,1.00,none,none,none,
admission,admission,3.55,Noun,9,7,3,admission,8.84,1.00,8.84,1.00,none,9.07,none,
admissions,admissions,2.18,Noun,10,8,3,admission,none,none,8.84,1.00,none,9.07,none,
admit,admit,59.20,Verb,5,5,2,admit,7.56,1.00,7.56,1.00,6.39,none,none,7.34
admits,admits,2.06,Verb,6,6,2,admit,none,none,7.56,1.00,6.39,none,none,7.34
admittance,admittance,0.67,Noun,10,8,3,admittance,10.39,1.00,10.39,1.00,none,none,none,
admittances,admittances,0.04,Noun,11,10,4,admittance,none,none,10.39,1.00,none,none,none,
admitted,admitted,9.16,Verb,8,7,3,admit,none,none,7.56,1.00,6.39,none,none,7.34
admitting,admitting,3.08,Verb,9,7,3,admit,none,none,7.56,1.00,6.39,none,none,7.34
admixture,admixture,0.02,Noun,9,10,3,admixture,14.10,0.53,14.10,0.53,none,none,none,
admonish,admonish,0.08,Verb,8,7,3,admonish,13.40,0.95,13.40,0.95,11.23,none,none,
admonished,admonished,0.08,Verb,10,8,3,admonish,none,none,13.40,0.95,11.23,none,none,
admonition,admonition,0.10,Noun,10,9,4,admonition,13.86,0.78,13.86,0.78,none,none,none,
admonitions,admonitions,0.02,Noun,11,10,4,admonition,none,none,13.86,0.78,none,none,none,
ado,ado,1.51,Noun,3,3,2,ado,11.88,0.85,11.88,0.85,none,none,none,12.22
adobe,adobe,0.47,Noun,5,5,3,adobe,12.22,0.95,12.22,0.95,none,none,none,
adolescence,adolescence,0.86,Noun,11,9,4,adolescence,10.42,1.00,10.42,1.00,none,11.70,none,
adolescences,adolescences,0.02,Noun,12,11,5,adolescence,none,none,10.42,1.00,none,11.70,none,
adolescent,adolescent,1.84,Adjective,10,9,4,adolescent,11.05,1.00,11.05,1.00,none,none,none,
adolescents,adolescents,0.57,Noun,11,10,4,adolescent,none,none,11.05,1.00,none,none,none,
adopt,adopt,4.67,Verb,5,5,2,adopt,7.83,1.00,7.83,1.00,none,8.26,none,8.12
adoptable,adoptable,0.02,Adjective,9,9,4,adoptable,9.22,1.00,9.22,1.00,none,none,none,
adopted,adopted,8.43,Verb,7,7,3,adopt,none,none,7.83,1.00,none,8.26,none,8.12
adopter,adopter,0.02,Noun,7,6,3,adopter,10.16,1.00,10.16,1.00,none,none,none,
adopting,adopting,1.18,Verb,8,7,3,adopt,none,none,7.83,1.00,none,8.26,none,8.12
adoption,adoption,4.69,Noun,8,7,3,adoption,7.79,1.00,7.79,1.00,none,none,none,
adoptions,adoptions,0.14,Noun,9,8,3,adoption,none,none,7.79,1.00,none,none,none,
adoptive,adoptive,0.59,Adjective,8,7,3,adoptive,9.16,1.00,9.16,1.00,none,none,none,
adopts,adopts,0.22,Verb,6,6,2,adopt,none,none,7.83,1.00,none,8.26,none,8.12
adorability,adorability,0.02,Noun,11,11,6,adorability,11.76,0.89,11.76,0.89,none,none,none,
adorable,adorable,10.53,Adjective,8,8,4,adorable,6.94,1.00,6.94,1.00,none,none,none,
adorableness,adorableness,0.02,Noun,12,11,5,adorableness,8.32,1.00,8.32,1.00,none,none,none,
adoration,adoration,0.59,Noun,9,7,4,adoration,11.80,0.95,11.80,0.95,none,none,none,
adore,adore,7.73,Verb,5,4,2,adore,7.95,1.00,7.95,1.00,7.64,none,none,8.70
adored,adored,2.45,Verb,6,5,2,adore,none,none,7.95,1.00,7.64,none,none,8.70
adores,adores,1.78,Verb,6,5,2,adore,none,none,7.95,1.00,7.64,none,none,8.70
adoring,adoring,0.69,Adjective,7,6,3,adoring,8.53,1.00,8.53,1.00,none,none,none,
adorn,adorn,0.16,Verb,5,5,2,adorn,11.65,0.94,11.65,0.94,none,none,none,
adorned,adorned,0.16,Verb,7,6,2,adorn,none,none,11.65,0.94,none,none,none,
adornment,adornment,0.08,Noun,9,9,3,adornment,12.32,1.00,12.32,1.00,none,none,none,
adornments,adornments,0.04,Noun,10,10,3,adornment,none,none,12.32,1.00,none,none,none,
adorns,adorns,0.06,Verb,6,6,2,adorn,none,none,11.65,0.94,none,none,none,
adrenal,adrenal,0.31,Adjective,7,7,3,adrenal,14.24,0.89,14.24,0.89,none,none,none,
adrenalin,adrenalin,0.55,Noun,9,9,4,adrenalin,11.65,0.94,11.65,0.94,none,none,none,
adrenaline,adrenaline,3.29,Noun,10,9,4,adrenaline,11.63,0.95,11.63,0.95,none,none,none,
adrift,adrift,1.00,Adjective,6,6,2,adrift,11.21,0.95,11.21,0.95,none,none,none,11.09
adroit,adroit,0.12,Adjective,6,5,2,adroit,12.75,0.42,12.75,0.42,none,none,none,
adroitness,adroitness,0.02,Noun,10,8,3,adroitness,15.21,0.74,15.21,0.74,none,none,none,
ads,ads,5.06,Noun,3,3,1,ad,none,none,8.22,1.00,none,none,7.42,
adulation,adulation,0.27,Noun,9,8,4,adulation,12.72,0.95,12.72,0.95,none,none,none,
adult,adult,14.29,Noun,5,5,2,adult,4.68,1.00,4.68,1.00,5.40,none,none,5.49
adulterant,adulterant,0.02,Noun,10,9,4,adulterant,12.86,0.78,12.86,0.78,none,none,none,
adulterate,adulterate,0.02,Verb,10,8,4,adulterate,13.76,0.94,13.76,0.94,none,none,none,
adulterer,adulterer,0.39,Noun,9,7,4,adulterer,11.63,1.00,11.63,1.00,none,none,none,
adulterers,adulterers,0.14,Noun,10,8,4,adulterer,none,none,11.63,1.00,none,none,none,
adulteress,adulteress,0.25,Noun,10,8,4,adulteress,12.22,1.00,12.22,1.00,none,none,none,
adulterous,adulterous,0.33,Adjective,10,8,4,adulterous,11.65,1.00,11.65,1.00,none,none,none,
adultery,adultery,1.94,Noun,8,7,4,adultery,11.84,1.00,11.84,1.00,none,11.43,none,
adulthood,adulthood,0.73,Noun,9,8,3,adulthood,9.95,1.00,9.95,1.00,none,none,none,
adults,adults,9.57,Noun,6,6,2,adult,none,none,4.68,1.00,5.40,none,none,5.49
adumbration,adumbration,Err:512,Err:512,11,9,4,adumbration,16.50,0.10,16.50,0.10,none,none,none,
advance,advance,15.98,Noun,7,6,2,advance,8.35,1.00,8.35,1.00,7.94,none,none,9.13
advanced,advanced,8.84,Adjective,8,7,2,advanced,9.05,1.00,9.05,1.00,8.17,none,none,
advancement,advancement,1.24,Noun,11,10,3,advancement,10.89,0.95,10.89,0.95,none,none,none,
advancements,advancements,0.16,Noun,12,11,3,advancement,none,none,10.89,0.95,none,none,none,
advances,advances,2.90,Noun,8,8,3,advance,none,none,8.35,1.00,7.94,none,none,9.13
advantage,advantage,22.06,Noun,9,8,3,advantage,9.32,1.00,9.32,1.00,none,none,none,
advantageous,advantageous,0.39,Adjective,12,10,4,advantageous,12.62,0.84,12.62,0.84,none,none,none,
advantages,advantages,2.82,Noun,10,10,4,advantage,none,none,9.32,1.00,none,none,none,
advent,advent,0.47,Noun,6,6,2,advent,10.38,0.89,10.38,0.89,none,none,none,
adventure,adventure,13.43,Noun,9,7,3,adventure,5.67,1.00,5.67,1.00,none,none,none,
adventurer,adventurer,0.73,Noun,10,8,4,adventurer,7.79,1.00,7.79,1.00,none,8.09,none,
adventurers,adventurers,0.22,Noun,11,9,4,adventurer,none,none,7.79,1.00,none,8.09,none,
adventures,adventures,3.61,Noun,10,8,3,adventure,none,none,5.67,1.00,none,none,none,
adventuresome,adventuresome,0.06,Adjective,13,10,4,adventuresome,10.38,1.00,10.38,1.00,none,none,none,
adventuress,adventuress,0.22,Noun,11,9,4,adventuress,10.44,0.95,10.44,0.95,none,none,none,
adventurist,adventurist,0.02,Noun,11,10,4,adventurist,8.68,1.00,8.68,1.00,none,none,none,
adventurous,adventurous,1.22,Adjective,11,9,4,adventurous,10.00,1.00,10.00,1.00,none,none,none,
adventurousness,adventurousness,0.02,Noun,15,12,5,adventurousness,11.81,1.00,11.81,1.00,none,none,none,
adverb,adverb,0.20,Noun,6,5,2,adverb,8.14,1.00,8.14,1.00,none,none,none,
adverbial,adverbial,0.06,Adjective,9,8,4,adverbial,13.14,0.78,13.14,0.78,none,none,none,
adverbs,adverbs,0.10,Noun,7,6,2,adverb,none,none,8.14,1.00,none,none,none,
adversarial,adversarial,0.24,Adjective,11,10,5,adversarial,14.00,1.00,14.00,1.00,none,none,none,
adversaries,adversaries,0.61,Noun,11,9,4,adversary,none,none,12.67,1.00,none,none,none,
adversary,adversary,1.51,Noun,9,8,4,adversary,12.67,1.00,12.67,1.00,none,none,none,
adverse,adverse,0.71,Adjective,7,5,2,adverse,10.72,1.00,10.72,1.00,none,11.89,none,12.53
adversities,adversities,0.06,Noun,11,9,4,adversity,none,none,11.79,1.00,none,none,none,
adversity,adversity,1.06,Noun,9,8,4,adversity,11.79,1.00,11.79,1.00,none,none,none,
advert,advert,0.25,Noun,6,5,2,advert,14.22,0.95,14.22,0.95,none,none,none,
advertise,advertise,1.65,Verb,9,7,3,advertise,9.63,1.00,9.63,1.00,none,none,none,
advertised,advertised,1.02,Verb,10,8,3,advertise,none,none,9.63,1.00,none,none,none,
advertisement,advertisement,1.20,Noun,13,11,4,advertisement,7.67,1.00,7.67,1.00,none,none,none,
advertisements,advertisements,0.49,Noun,14,12,4,advertisement,none,none,7.67,1.00,none,none,none,
advertiser,advertiser,0.16,Noun,10,8,4,advertiser,11.00,1.00,11.00,1.00,none,none,none,
advertisers,advertisers,0.47,Noun,11,9,4,advertiser,none,none,11.00,1.00,none,none,none,
advertises,advertises,0.14,Verb,10,9,4,advertise,none,none,9.63,1.00,none,none,none,
advertising,advertising,7.43,Noun,11,9,4,advertising,9.95,1.00,9.95,1.00,none,none,none,
adverts,adverts,0.08,Noun,7,6,2,advert,none,none,14.22,0.95,none,none,none,
advice,advice,47.98,Noun,6,5,2,advice,8.61,1.00,8.61,1.00,none,8.45,none,8.70
advices,advices,0.06,Noun,7,7,3,advice,none,none,8.61,1.00,none,8.45,none,8.70
advisable,advisable,0.78,Adjective,9,9,4,advisable,10.75,1.00,10.75,1.00,none,none,none,
advise,advise,12.20,Verb,6,5,2,advise,8.89,1.00,8.89,1.00,8.17,none,none,9.58
advised,advised,5.24,Verb,7,6,2,advise,none,none,8.89,1.00,8.17,none,none,9.58
advisee,advisee,0.25,Noun,7,6,3,advisee,12.89,0.90,12.89,0.90,none,none,none,
advisees,advisees,0.43,Noun,8,7,3,advisee,none,none,12.89,0.90,none,none,none,
advisement,advisement,0.76,Noun,10,9,3,advisement,12.50,1.00,12.50,1.00,none,none,none,
adviser,adviser,1.04,Noun,7,6,3,adviser,10.78,1.00,10.78,1.00,none,none,none,
advisers,advisers,0.25,Noun,8,7,3,adviser,none,none,10.78,1.00,none,none,none,
advises,advises,0.47,Verb,7,7,3,advise,none,none,8.89,1.00,8.17,none,none,9.58
advising,advising,1.33,Verb,8,7,3,advise,none,none,8.89,1.00,8.17,none,none,9.58
advisor,advisor,4.41,Noun,7,6,3,advisor,11.24,1.00,11.24,1.00,none,none,none,
advisories,advisories,0.04,Verb,10,8,4,advisory,none,none,11.55,1.00,none,none,none,
advisors,advisors,1.29,Noun,8,7,3,advisor,none,none,11.24,1.00,none,none,none,
advisory,advisory,1.00,Adjective,8,7,4,advisory,11.55,1.00,11.55,1.00,none,none,none,
advocacy,advocacy,0.29,Noun,8,8,4,advocacy,14.06,0.89,14.06,0.89,none,none,none,
advocate,advocate,2.22,Noun,8,7,3,advocate,11.11,1.00,11.11,1.00,none,none,none,
advocated,advocated,0.35,Verb,9,9,4,advocate,none,none,11.11,1.00,none,none,none,
advocates,advocates,0.33,Noun,9,8,3,advocate,none,none,11.11,1.00,none,none,none,
advocating,advocating,0.33,Verb,10,9,4,advocate,none,none,11.11,1.00,none,none,none,
aegis,aegis,0.10,Noun,5,4,2,aegis,17.00,0.37,17.00,0.37,none,none,none,
aerate,aerate,0.02,Verb,6,4,3,aerate,13.44,0.84,13.44,0.84,none,none,none,
aeration,aeration,0.02,Noun,8,6,4,aeration,12.35,0.89,12.35,0.89,none,none,none,
aerial,aerial,2.53,Noun,6,5,3,aerial,11.50,0.95,11.50,0.95,none,8.51,none,
aerialist,aerialist,0.02,Noun,9,8,5,aerialist,14.08,0.72,14.08,0.72,none,none,none,
aerials,aerials,0.18,Noun,7,6,3,aerial,none,none,11.50,0.95,none,8.51,none,
aerie,aerie,0.02,Noun,5,3,2,aerie,13.00,0.61,13.00,0.61,none,none,none,
aeries,aeries,0.02,Noun,6,4,3,aerie,none,none,13.00,0.61,none,none,none,
aero,aero,0.10,Noun,4,3,2,aero,11.40,1.00,11.40,1.00,none,none,none,
aerobatics,aerobatics,0.02,Noun,10,9,4,aerobatics,9.53,0.89,9.53,0.89,none,none,none,
aerobic,aerobic,0.18,Adjective,7,6,3,aerobic,10.24,0.94,10.24,0.94,none,none,none,
aerobics,aerobics,1.55,Noun,8,6,3,aerobics,11.00,1.00,11.00,1.00,none,none,none,
aerodynamic,aerodynamic,0.43,Adjective,11,10,5,aerodynamic,13.40,1.00,13.40,1.00,none,none,none,
aerodynamics,aerodynamics,0.31,Noun,12,11,5,aerodynamics,11.50,1.00,11.50,1.00,none,none,none,
aeronaut,aeronaut,Err:512,Err:512,8,6,3,aeronaut,12.92,0.60,12.92,0.60,none,none,none,
aeronautical,aeronautical,0.24,Adjective,12,10,5,aeronautical,14.35,0.89,14.35,0.89,none,none,none,
aeronautics,aeronautics,0.18,Noun,11,9,4,aeronautics,12.35,0.89,12.35,0.89,none,none,none,
aerosol,aerosol,0.27,Noun,7,6,3,aerosol,9.47,1.00,9.47,1.00,none,none,none,
aerosols,aerosols,0.02,Noun,8,7,3,aerosol,none,none,9.47,1.00,none,none,none,
aerospace,aerospace,1.08,Noun,9,7,3,aerospace,12.06,0.94,12.06,0.94,none,none,none,
aesthete,aesthete,0.12,Noun,8,5,2,aesthete,14.90,0.50,14.90,0.50,none,none,none,
aesthetes,aesthetes,0.02,Noun,9,6,2,aesthete,none,none,14.90,0.50,none,none,none,
aesthetic,aesthetic,0.67,Adjective,9,7,3,aesthetic,15.74,1.00,15.74,1.00,none,none,none,
aesthetics,aesthetics,0.29,Noun,10,8,3,aesthetics,15.06,1.00,15.06,1.00,none,none,none,
afar,afar,1.16,Adverb,4,4,2,afar,8.21,1.00,8.21,1.00,none,8.96,none,9.67
afeard,afeard,0.22,Noun,6,5,2,afeard,10.86,0.39,10.86,0.39,none,none,none,
affability,affability,0.08,Noun,10,9,5,affability,14.07,0.67,14.07,0.67,none,none,none,
affable,affable,0.12,Adjective,7,6,3,affable,13.40,0.83,13.40,0.83,none,none,none,
affair,affair,29.45,Noun,6,4,2,affair,10.94,1.00,10.94,1.00,10.04,none,none,10.32
affaire,affaire,0.02,Noun,7,4,2,affaire,11.45,0.58,11.45,0.58,none,none,none,
affaires,affaires,0.06,Noun,8,5,2,affaire,none,none,11.45,0.58,none,none,none,
affairs,affairs,14.65,Noun,7,5,2,affair,none,none,10.94,1.00,10.04,none,none,10.32
affect,affect,11.14,Verb,6,5,2,affect,9.60,1.00,9.60,1.00,none,8.77,none,8.12
affectation,affectation,0.14,Noun,11,9,4,affectation,13.82,0.89,13.82,0.89,none,none,none,
affectations,affectations,0.04,Noun,12,10,4,affectation,none,none,13.82,0.89,none,none,none,
affected,affected,6.76,Verb,8,7,3,affect,none,none,9.60,1.00,none,8.77,none,8.12
affecting,affecting,2.55,Verb,9,7,3,affect,none,none,9.60,1.00,none,8.77,none,8.12
affection,affection,8.45,Noun,9,7,3,affection,8.53,1.00,8.53,1.00,none,9.24,none,
affectionate,affectionate,2.25,Adjective,12,9,4,affectionate,8.50,1.00,8.50,1.00,8.11,none,none,
affections,affections,1.20,Noun,10,8,3,affection,none,none,8.53,1.00,none,9.24,none,
affective,affective,0.08,Adjective,9,7,3,affective,11.74,1.00,11.74,1.00,none,none,none,
affectless,affectless,0.02,Adjective,10,8,3,affectless,13.60,0.79,13.60,0.79,none,none,none,
affects,affects,4.78,Verb,7,6,2,affect,none,none,9.60,1.00,none,8.77,none,8.12
affiant,affiant,0.02,Noun,7,6,3,affiant,14.00,0.56,14.00,0.56,none,none,none,
affidavit,affidavit,1.96,Noun,9,8,4,affidavit,15.69,0.84,15.69,0.84,12.92,none,none,
affidavits,affidavits,0.61,Noun,10,9,4,affidavit,none,none,15.69,0.84,12.92,none,none,
affiliate,affiliate,0.29,Noun,9,7,4,affiliate,12.00,0.95,12.00,0.95,none,none,none,
affiliated,affiliated,0.78,Verb,10,9,5,affiliate,none,none,12.00,0.95,none,none,none,
affiliates,affiliates,0.33,Noun,10,8,4,affiliate,none,none,12.00,0.95,none,none,none,
affiliation,affiliation,0.49,Noun,11,9,5,affiliation,12.28,1.00,12.28,1.00,none,none,none,
affiliations,affiliations,0.27,Noun,12,10,5,affiliation,none,none,12.28,1.00,none,none,none,
affinity,affinity,0.57,Noun,8,7,4,affinity,12.22,1.00,12.22,1.00,none,none,none,
affirm,affirm,0.33,Verb,6,4,2,affirm,12.24,0.94,12.24,0.94,11.98,none,none,
affirmation,affirmation,0.53,Noun,11,8,4,affirmation,12.56,1.00,12.56,1.00,none,11.43,none,
affirmations,affirmations,0.20,Noun,12,9,4,affirmation,none,none,12.56,1.00,none,11.43,none,
affirmative,affirmative,4.73,Noun,11,8,4,affirmative,10.67,1.00,10.67,1.00,none,none,none,
affirmed,affirmed,0.14,Verb,8,5,2,affirm,none,none,12.24,0.94,11.98,none,none,
affirming,affirming,0.06,Verb,9,6,3,affirm,none,none,12.24,0.94,11.98,none,none,
affirms,affirms,0.04,Verb,7,5,2,affirm,none,none,12.24,0.94,11.98,none,none,
affix,affix,0.12,Verb,5,5,2,affix,11.94,1.00,11.94,1.00,none,none,none,
afflict,afflict,0.08,Verb,7,6,2,afflict,11.50,1.00,11.50,1.00,none,none,none,
afflicting,afflicting,0.12,Verb,10,8,3,afflict,none,none,11.50,1.00,none,none,none,
affliction,affliction,1.18,Noun,10,8,3,affliction,11.32,1.00,11.32,1.00,10.94,none,none,
afflictions,afflictions,0.16,Noun,11,9,3,affliction,none,none,11.32,1.00,10.94,none,none,
afflictive,afflictive,0.02,Adjective,10,8,3,afflictive,14.40,0.68,14.40,0.68,none,none,none,
afflicts,afflicts,0.06,Verb,8,7,2,afflict,none,none,11.50,1.00,none,none,none,
affluence,affluence,0.14,Noun,9,7,3,affluence,13.88,0.89,13.88,0.89,none,none,none,
affluent,affluent,0.29,Adjective,8,7,3,affluent,13.16,1.00,13.16,1.00,none,none,none,
afford,afford,44.43,Verb,6,5,2,afford,7.47,1.00,7.47,1.00,none,8.96,none,9.22
affordability,affordability,0.02,Noun,13,12,6,affordability,10.89,1.00,10.89,1.00,none,none,none,
affordable,affordable,0.65,Adjective,10,9,4,affordable,8.15,1.00,8.15,1.00,none,none,none,
afforded,afforded,0.53,Verb,8,7,3,afford,none,none,7.47,1.00,none,8.96,none,9.22
affording,affording,0.16,Verb,9,7,3,afford,none,none,7.47,1.00,none,8.96,none,9.22
affords,affords,0.27,Verb,7,6,2,afford,none,none,7.47,1.00,none,8.96,none,9.22
affray,affray,Err:512,Err:512,6,5,3,affray,13.00,0.28,13.00,0.28,none,none,none,
affright,affright,0.04,Noun,8,5,2,affright,12.77,0.72,12.77,0.72,none,12.81,none,
affront,affront,0.59,Noun,7,6,2,affront,12.44,0.86,12.44,0.86,none,none,none,11.59
afghan,afghan,0.57,Adjective,6,5,2,afghan,9.11,1.00,9.11,1.00,none,none,none,
afghans,afghans,0.20,Noun,7,6,2,Afghan,none,none,9.11,1.00,none,none,none,
aficionado,aficionado,0.22,Noun,10,10,5,aficionado,13.86,0.78,13.86,0.78,none,none,none,
aficionados,aficionados,0.10,Noun,11,10,5,aficionado,none,none,13.86,0.78,none,none,none,
afire,afire,0.49,Adjective,5,4,2,afire,11.87,0.83,11.87,0.83,none,none,none,
aflame,aflame,0.33,Adjective,6,5,2,aflame,11.22,1.00,11.22,1.00,none,none,none,
afloat,afloat,1.67,Adjective,6,5,2,afloat,9.33,1.00,9.33,1.00,none,none,none,9.40
afoot,afoot,1.02,Adverb,5,4,2,afoot,11.48,0.95,11.48,0.95,none,none,none,10.90
aforementioned,aforementioned,0.47,Adjective,14,11,4,aforementioned,14.58,0.95,14.58,0.95,none,none,none,
aforesaid,aforesaid,0.08,Adjective,9,7,3,aforesaid,14.44,0.47,14.44,0.47,none,none,none,
aforethought,aforethought,0.06,Adjective,12,7,3,aforethought,13.72,0.95,13.72,0.95,none,none,none,
afoul,afoul,0.22,Noun,5,4,2,afoul,11.81,0.89,11.81,0.89,none,none,none,
afraid,afraid,247.67,Adjective,6,5,2,afraid,4.42,1.00,4.42,1.00,4.73,none,none,5.62
afro,afro,1.12,Noun,4,4,2,afro,9.58,1.00,9.58,1.00,none,none,none,
afros,afros,0.06,Noun,5,5,2,Afro,none,none,9.58,1.00,none,none,none,
aft,aft,3.53,Adverb,3,3,1,aft,13.88,0.76,13.88,0.76,none,none,12.75,
after,after,682.59,Preposition,5,4,2,after,6.00,1.00,6.00,1.00,4.33,none,none,5.37
afterbirth,afterbirth,0.10,Noun,10,7,3,afterbirth,14.06,0.89,14.06,0.89,none,none,none,
afterburner,afterburner,0.04,Noun,11,8,4,afterburner,10.69,0.84,10.69,0.84,none,none,none,
afterburners,afterburners,0.06,Noun,12,9,4,afterburner,none,none,10.69,0.84,none,none,none,
aftercare,aftercare,0.10,Noun,9,7,3,aftercare,11.62,0.89,11.62,0.89,none,none,none,
afterdeck,afterdeck,0.04,Noun,9,7,3,afterdeck,15.86,0.37,15.86,0.37,none,none,none,
aftereffect,after-effect,0.06,Noun,11,9,4,aftereffect,12.06,0.94,12.06,0.94,none,none,none,
aftereffects,after-effects,0.14,Noun,12,10,4,aftereffect,none,none,12.06,0.94,none,none,none,
afterglow,afterglow,0.31,Noun,9,7,3,afterglow,12.53,0.89,12.53,0.89,none,none,none,
afterimage,afterimage,0.04,Noun,10,8,4,afterimage,13.67,0.50,13.67,0.50,none,none,none,
afterlife,afterlife,1.59,Noun,9,7,3,afterlife,8.11,1.00,8.11,1.00,none,none,none,
aftermarket,aftermarket,0.04,Noun,11,10,4,aftermarket,14.20,1.00,14.20,1.00,none,none,none,
aftermath,aftermath,1.04,Noun,9,7,3,aftermath,12.05,1.00,12.05,1.00,none,none,none,
aftermaths,aftermaths,0.02,Noun,10,8,3,aftermath,none,none,12.05,1.00,none,none,none,
afternoon,afternoon,91.49,Noun,9,7,3,afternoon,4.65,1.00,4.65,1.00,none,5.73,none,
afternoons,afternoons,1.98,Noun,10,8,3,afternoon,none,none,4.65,1.00,none,5.73,none,
afterschool,afterschool,0.10,Noun,11,8,3,afterschool,6.89,1.00,6.89,1.00,none,none,none,
aftershave,aftershave,1.08,Noun,10,7,3,aftershave,10.42,1.00,10.42,1.00,none,none,none,
aftershaves,aftershaves,0.02,Noun,11,8,3,aftershave,none,none,10.42,1.00,none,none,none,
aftershock,aftershock,0.24,Noun,10,7,3,aftershock,10.33,1.00,10.33,1.00,none,none,none,
aftershocks,aftershocks,0.02,Noun,11,8,3,aftershock,none,none,10.33,1.00,none,none,none,
aftertaste,aftertaste,0.22,Noun,10,8,3,aftertaste,10.00,1.00,10.00,1.00,none,none,none,
afterthought,afterthought,0.25,Noun,12,7,3,afterthought,12.78,1.00,12.78,1.00,none,none,none,
afterthoughts,afterthoughts,0.04,Noun,13,8,3,afterthought,none,none,12.78,1.00,none,none,none,
afterwards,afterwards,14.00,Adverb,10,8,3,afterwards,7.71,1.00,7.71,1.00,none,none,none,
afterworld,afterworld,0.02,Noun,10,8,3,afterworld,10.41,0.94,10.41,0.94,none,none,none,
again,again,792.71,Adverb,5,4,2,again,5.78,1.00,5.78,1.00,4.05,none,none,5.31
against,against,201.73,Preposition,7,6,2,against,7.97,1.00,7.97,1.00,5.37,none,none,6.82
agate,agate,0.04,Noun,5,4,2,agate,13.00,0.83,13.00,0.83,none,none,none,
agates,agates,0.02,Noun,6,5,2,agate,none,none,13.00,0.83,none,none,none,
agave,agave,0.04,Verb,5,4,2,agave,14.57,0.74,14.57,0.74,none,none,none,
age,age,79.20,Noun,3,2,1,age,4.38,1.00,4.38,1.00,3.99,5.30,4.80,
ageism,ageism,0.24,Noun,6,6,3,ageism,17.07,0.74,17.07,0.74,none,none,none,
ageist,ageist,0.08,Noun,6,5,2,ageist,15.79,0.78,15.79,0.78,none,none,none,
ageless,ageless,0.29,Adjective,7,5,2,ageless,10.30,1.00,10.30,1.00,none,none,none,
agencies,agencies,3.22,Noun,8,7,3,agency,none,none,10.58,1.00,none,10.87,none,
agency,agency,19.43,Noun,6,6,3,agency,10.58,1.00,10.58,1.00,none,10.87,none,
agenda,agenda,7.31,Noun,6,6,3,agenda,9.95,1.00,9.95,1.00,none,none,none,
agendas,agendas,0.41,Noun,7,7,3,agenda,none,none,9.95,1.00,none,none,none,
agent,agent,102.65,Noun,5,5,2,agent,9.55,1.00,9.55,1.00,none,9.22,none,9.22
agents,agents,21.06,Noun,6,6,2,agent,none,none,9.55,1.00,none,9.22,none,9.22
ageratum,ageratum,0.02,Noun,8,8,4,ageratum,13.29,0.35,13.29,0.35,none,none,none,
ages,ages,11.12,Noun,4,4,2,age,none,none,4.38,1.00,3.99,5.30,4.80,
agglomerate,agglomerate,Err:512,Err:512,11,8,4,agglomerate,15.67,0.32,15.67,0.32,none,none,none,
agglutinate,agglutinate,0.02,Verb,11,9,4,agglutinate,14.89,0.45,14.89,0.45,none,none,none,
aggrandize,aggrandize,0.02,Verb,10,8,3,aggrandize,14.83,0.63,14.83,0.63,none,none,none,
aggrandizement,aggrandisement,0.02,Noun,14,12,4,aggrandizement,14.75,0.42,14.75,0.42,none,none,none,
aggravate,aggravate,0.73,Verb,9,7,3,aggravate,8.32,1.00,8.32,1.00,none,none,none,
aggravated,aggravated,1.25,Adjective,10,9,4,aggravated,8.61,1.00,8.61,1.00,none,none,none,
aggravates,aggravates,0.18,Verb,10,8,3,aggravate,none,none,8.32,1.00,none,none,none,
aggravating,aggravating,0.47,Verb,11,9,4,aggravate,none,none,8.32,1.00,none,none,none,
aggravation,aggravation,1.16,Noun,11,9,4,aggravation,9.11,1.00,9.11,1.00,none,none,none,
aggregate,aggregate,0.20,Noun,9,7,3,aggregate,13.67,0.86,13.67,0.86,none,none,none,
aggregation,aggregation,0.04,Noun,11,9,4,aggregation,14.56,0.90,14.56,0.90,none,none,none,
aggress,aggress,0.04,Verb,7,5,2,aggress,12.13,0.79,12.13,0.79,none,none,none,
aggresses,aggresses,0.04,Verb,9,7,3,aggress,none,none,12.13,0.79,none,none,none,
aggression,aggression,2.80,Noun,10,7,3,aggression,9.30,1.00,9.30,1.00,none,none,none,
aggressions,aggressions,0.08,Noun,11,8,3,aggression,none,none,9.30,1.00,none,none,none,
aggressive,aggressive,7.94,Adjective,10,7,3,aggressive,8.32,1.00,8.32,1.00,none,none,none,
aggressiveness,aggressiveness,0.39,Noun,14,10,4,aggressiveness,11.06,1.00,11.06,1.00,none,none,none,
aggressor,aggressor,0.57,Noun,9,6,3,aggressor,11.47,1.00,11.47,1.00,none,11.55,none,
aggressors,aggressors,0.29,Noun,10,7,3,aggressor,none,none,11.47,1.00,none,11.55,none,
aggrieved,aggrieved,0.20,Adjective,9,6,2,aggrieved,12.38,0.84,12.38,0.84,none,none,none,
aghast,aghast,0.14,Adjective,6,5,2,aghast,12.00,0.82,12.00,0.82,none,none,none,
agile,agile,0.39,Adjective,5,4,2,agile,11.94,0.95,11.94,0.95,none,none,none,
agility,agility,0.45,Noun,7,7,4,agility,10.06,0.95,10.06,0.95,none,none,none,
aging,ageing,2.57,Adjective,5,4,2,aging,7.35,1.00,7.35,1.00,none,none,none,
agitate,agitate,0.43,Verb,7,6,3,agitate,10.17,1.00,10.17,1.00,none,none,none,
agitated,agitated,2.51,Adjective,8,8,4,agitated,10.12,1.00,10.12,1.00,none,none,none,
agitates,agitates,0.12,Verb,8,7,3,agitate,none,none,10.17,1.00,none,none,none,
agitation,agitation,0.49,Noun,9,8,4,agitation,11.50,1.00,11.50,1.00,none,none,none,
agitator,agitator,0.22,Noun,8,7,4,agitator,11.84,1.00,11.84,1.00,none,none,none,
agitators,agitators,0.25,Noun,9,8,4,agitator,none,none,11.84,1.00,none,none,none,
agitprop,agitprop,0.02,Noun,8,8,3,agitprop,16.60,0.26,16.60,0.26,none,none,none,
agleam,agleam,Err:512,Err:512,6,5,2,agleam,14.71,0.33,14.71,0.33,none,none,none,
aglitter,aglitter,Err:512,Err:512,8,6,3,aglitter,13.67,0.63,13.67,0.63,none,none,none,
aglow,aglow,0.47,Adjective,5,4,2,aglow,10.06,0.81,10.06,0.81,none,none,none,
agnate,agnate,0.10,Noun,6,5,2,agnate,16.50,0.22,16.50,0.22,none,none,none,
agnates,agnates,0.08,Noun,7,6,2,agnate,none,none,16.50,0.22,none,none,none,
agnostic,agnostic,0.37,Noun,8,8,3,agnostic,13.80,0.91,13.80,0.91,none,none,none,
ago,ago,286.12,Adverb,3,3,2,ago,7.12,1.00,7.12,1.00,none,none,none,6.06
agog,agog,0.12,Adjective,4,4,2,agog,14.73,0.58,14.73,0.58,none,none,none,
agonies,agonies,0.24,Noun,7,6,3,agony,none,none,9.22,1.00,none,9.13,none,
agonist,agonist,0.02,Noun,7,7,3,agonist,15.06,0.89,15.06,0.89,none,none,none,
agonize,agonise,0.10,Verb,7,6,3,agonize,11.58,1.00,11.58,1.00,none,none,none,
agonizing,agonising,0.71,Adjective,9,8,4,agonizing,10.89,1.00,10.89,1.00,none,none,none,
agony,agony,3.75,Noun,5,5,3,agony,9.22,1.00,9.22,1.00,none,9.13,none,
agora,agora,0.02,Noun,5,4,3,agora,13.73,0.58,13.73,0.58,none,none,none,
agoraphobia,agoraphobia,0.14,Noun,11,10,6,agoraphobia,14.44,0.95,14.44,0.95,none,none,none,
agoraphobic,agoraphobic,0.18,Adjective,11,11,5,agoraphobic,14.64,0.70,14.64,0.70,none,none,none,
agoras,agoras,0.02,Noun,6,6,3,agora,none,none,13.73,0.58,none,none,none,
agrarian,agrarian,0.16,Adjective,8,8,4,agrarian,14.83,0.60,14.83,0.60,none,none,none,
agree,agree,64.67,Verb,5,4,2,agree,7.68,1.00,7.68,1.00,5.46,none,none,6.47
agreeable,agreeable,1.59,Adjective,9,8,4,agreeable,8.41,0.94,8.41,0.94,none,none,none,
agreed,agreed,35.92,Verb,6,5,2,agree,none,none,7.68,1.00,5.46,none,none,6.47
agreeing,agreeing,2.08,Verb,8,6,3,agree,none,none,7.68,1.00,5.46,none,none,6.47
agreement,agreement,17.49,Noun,9,8,3,agreement,8.16,1.00,8.16,1.00,none,none,none,
agreements,agreements,0.80,Noun,10,9,3,agreement,none,none,8.16,1.00,none,none,none,
agrees,agrees,4.29,Verb,6,5,2,agree,none,none,7.68,1.00,5.46,none,none,6.47
agribusiness,agribusiness,0.14,Noun,12,10,4,agribusiness,18.07,0.78,18.07,0.78,none,none,none,
agricultural,agricultural,0.94,Adjective,12,11,5,agricultural,10.11,1.00,10.11,1.00,none,none,none,
agriculturalist,agriculturalist,0.04,Noun,15,14,6,agriculturalist,12.78,1.00,12.78,1.00,none,none,none,
agriculture,agriculture,1.10,Noun,11,9,4,agriculture,10.32,1.00,10.32,1.00,none,none,none,
agricultures,agricultures,0.02,Noun,12,10,4,agriculture,none,none,10.32,1.00,none,none,none,
ague,ague,0.04,Noun,4,4,2,ague,15.20,0.56,15.20,0.56,none,12.90,none,
ahead,ahead,198.33,Adverb,5,4,2,ahead,5.45,1.00,5.45,1.00,5.91,none,none,6.68
ahoy,ahoy,3.71,Noun,4,3,2,ahoy,7.61,1.00,7.61,1.00,none,none,none,
aid,aid,13.90,Noun,3,2,1,aid,7.33,1.00,7.33,1.00,7.57,none,6.51,
aide,aide,2.02,Noun,4,2,1,aide,7.83,0.95,7.83,0.95,none,10.87,9.96,
aided,aided,0.65,Verb,5,4,2,aid,none,none,7.33,1.00,7.57,none,6.51,
aider,aider,0.02,Noun,5,3,2,aider,12.92,0.72,12.92,0.72,none,none,none,
aides,aides,0.63,Noun,5,3,1,aide,none,none,7.83,0.95,none,10.87,9.96,
aiding,aiding,1.06,Verb,6,4,2,aid,none,none,7.33,1.00,7.57,none,6.51,
aids,aids,1.06,Noun,4,3,1,aids,8.89,1.00,8.89,1.00,none,none,none,
aigrette,aigrette,Err:512,Err:512,8,5,2,aigrette,15.00,0.05,15.00,0.05,none,none,none,
aikido,aikido,0.08,Noun,6,5,3,aikido,14.08,0.65,14.08,0.65,none,none,none,
ail,ail,0.22,Verb,3,2,1,ail,9.71,0.89,9.71,0.89,none,none,11.02,
ailed,ailed,0.04,Verb,5,3,1,ail,none,none,9.71,0.89,none,none,11.02,
aileron,aileron,0.02,Noun,7,5,3,aileron,14.71,0.39,14.71,0.39,none,none,none,
ailerons,ailerons,0.08,Noun,8,6,3,aileron,none,none,14.71,0.39,none,none,none,
ailing,ailing,0.51,Adjective,6,4,2,ailing,10.63,1.00,10.63,1.00,none,none,none,
ailment,ailment,0.51,Noun,7,6,2,ailment,10.37,1.00,10.37,1.00,none,none,none,11.39
ailments,ailments,0.61,Noun,8,7,2,ailment,none,none,10.37,1.00,none,none,none,11.39
ails,ails,0.76,Verb,4,3,1,ail,none,none,9.71,0.89,none,none,11.02,
aim,aim,14.86,Verb,3,2,1,aim,6.72,0.95,6.72,0.95,6.53,6.92,6.33,
aimed,aimed,1.94,Verb,5,3,1,aim,none,none,6.72,0.95,6.53,6.92,6.33,
aimer,aimer,0.06,Noun,5,3,2,aimer,9.85,0.68,9.85,0.68,10.84,none,none,
aiming,aiming,3.78,Verb,6,4,2,aim,none,none,6.72,0.95,6.53,6.92,6.33,
aimless,aimless,0.27,Adjective,7,5,2,aimless,10.32,1.00,10.32,1.00,none,none,none,
aims,aims,1.18,Verb,4,3,1,aim,none,none,6.72,0.95,6.53,6.92,6.33,
air,air,139.02,Noun,3,2,1,air,3.94,1.00,3.94,1.00,4.54,none,4.53,
airbag,airbag,0.39,Noun,6,5,2,airbag,12.26,1.00,12.26,1.00,none,none,none,
airbags,airbags,0.16,Noun,7,6,2,airbag,none,none,12.26,1.00,none,none,none,
airbase,airbase,0.10,Noun,7,5,2,airbase,10.35,1.00,10.35,1.00,none,none,none,
airboat,airboat,0.08,Noun,7,5,2,airboat,10.75,0.91,10.75,0.91,none,none,none,
airborne,airborne,4.22,Adjective,8,6,2,airborne,9.85,1.00,9.85,1.00,none,none,none,
airbrush,airbrush,0.18,Noun,8,7,3,airbrush,12.06,1.00,12.06,1.00,12.40,none,none,
aircraft,aircraft,9.45,Noun,8,7,2,aircraft,7.61,1.00,7.61,1.00,none,none,none,
aircrafts,aircrafts,0.22,Noun,9,8,2,aircraft,none,none,7.61,1.00,none,none,none,
aircraftsman,aircraftsman,0.02,Noun,12,12,4,aircraftsman,11.67,0.95,11.67,0.95,none,none,none,
aircrew,aircrew,0.06,Verb,7,5,2,aircrew,10.15,0.91,10.15,0.91,none,none,none,
airdrome,airdrome,0.04,Noun,8,7,3,airdrome,15.11,0.50,15.11,0.50,none,none,none,
airdromes,airdromes,0.02,Noun,9,8,3,airdrome,none,none,15.11,0.50,none,none,none,
airdrop,airdrop,0.04,Noun,7,6,2,airdrop,9.18,0.94,9.18,0.94,none,none,none,
aired,aired,0.88,Verb,5,3,1,air,none,none,3.94,1.00,4.54,none,4.53,
airfare,airfare,0.24,Noun,7,5,2,airfare,10.26,1.00,10.26,1.00,none,none,none,
airfares,airfares,0.04,Noun,8,6,2,airfare,none,none,10.26,1.00,none,none,none,
airfield,airfield,2.31,Noun,8,6,2,airfield,8.60,1.00,8.60,1.00,none,none,none,
airfields,airfields,0.14,Noun,9,7,2,airfield,none,none,8.60,1.00,none,none,none,
airflow,airflow,0.14,Noun,7,5,2,airflow,12.42,1.00,12.42,1.00,none,none,none,
airfoil,airfoil,0.02,Noun,7,5,2,airfoil,14.64,0.55,14.64,0.55,none,none,none,
airforce,airforce,0.20,Noun,8,6,2,airforce,6.83,1.00,6.83,1.00,none,none,none,
airhead,airhead,0.53,Noun,7,5,2,airhead,10.17,1.00,10.17,1.00,none,none,none,
airheads,airheads,0.12,Noun,8,7,3,airhead,none,none,10.17,1.00,none,none,none,
airiness,airiness,0.02,Noun,8,6,3,airiness,11.35,0.85,11.35,0.85,none,none,none,
airless,airless,0.10,Adjective,7,5,2,airless,10.00,0.94,10.00,0.94,none,none,none,
airlift,airlift,0.27,Noun,7,6,2,airlift,12.06,1.00,12.06,1.00,none,none,none,
airlifted,airlifted,0.20,Verb,9,8,3,airlift,none,none,12.06,1.00,none,none,none,
airlifting,airlifting,0.10,Verb,10,8,3,airlift,none,none,12.06,1.00,none,none,none,
airlifts,airlifts,0.02,Noun,8,7,2,airlift,none,none,12.06,1.00,none,none,none,
airline,airline,6.49,Noun,7,5,2,airline,8.74,1.00,8.74,1.00,none,none,none,
airliner,airliner,0.57,Noun,8,6,3,airliner,9.89,1.00,9.89,1.00,none,none,none,
airliners,airliners,0.14,Noun,9,7,3,airliner,none,none,9.89,1.00,none,none,none,
airlines,airlines,3.75,Noun,8,6,2,airline,none,none,8.74,1.00,none,none,none,
airlock,airlock,0.82,Noun,7,5,2,airlock,11.61,0.95,11.61,0.95,none,none,none,
airlocks,airlocks,0.02,Noun,8,6,2,airlock,none,none,11.61,0.95,none,none,none,
airmail,airmail,0.33,Noun,7,5,2,airmail,12.00,0.89,12.00,0.89,none,none,none,
airman,airman,0.59,Name,6,5,2,airman,10.58,1.00,10.58,1.00,8.06,none,none,
airmattress,air mattress,Err:512,Noun,11,9,3,airmattress,9.37,1.00,9.37,1.00,none,none,none,
airmen,airmen,0.67,Noun,6,5,2,airman,none,none,10.58,1.00,8.06,none,none,
airmobile,airmobile,0.02,Noun,9,8,4,airmobile,12.69,0.72,12.69,0.72,none,none,none,
airplane,aeroplane,10.92,Noun,8,6,2,airplane,3.94,1.00,3.94,1.00,none,none,none,
airplanes,aeroplanes,3.22,Noun,9,7,2,airplane,none,none,3.94,1.00,none,none,none,
airplay,airplay,0.22,Noun,7,6,3,airplay,12.93,0.75,12.93,0.75,none,none,none,
airport,airport,38.04,Noun,7,6,2,airport,6.84,1.00,6.84,1.00,none,5.18,none,6.13
airports,airports,1.37,Noun,8,7,2,airport,none,none,6.84,1.00,none,5.18,none,6.13
airs,airs,0.76,Noun,4,3,1,air,none,none,3.94,1.00,4.54,none,4.53,
airship,airship,0.59,Noun,7,5,2,airship,10.80,1.00,10.80,1.00,none,8.09,none,
airships,airships,0.08,Noun,8,6,2,airship,none,none,10.80,1.00,none,8.09,none,
airsick,airsick,0.31,Adjective,7,6,3,airsick,11.47,1.00,11.47,1.00,none,none,none,
airsickness,airsickness,0.04,Noun,11,9,4,airsickness,10.17,0.90,10.17,0.90,none,none,none,
airspace,airspace,1.61,Noun,8,6,2,airspace,11.50,1.00,11.50,1.00,none,none,none,
airspeed,airspeed,0.47,Noun,8,6,2,airspeed,10.90,1.00,10.90,1.00,none,none,none,
airstream,airstream,0.16,Noun,9,8,3,airstream,12.22,1.00,12.22,1.00,none,none,none,
airstrike,airstrike,0.14,Noun,9,7,2,airstrike,12.78,1.00,12.78,1.00,none,none,none,
airstrikes,airstrikes,0.04,Noun,10,8,2,airstrike,none,none,12.78,1.00,none,none,none,
airstrip,airstrip,1.12,Noun,8,7,2,airstrip,8.16,1.00,8.16,1.00,none,none,none,
airstrips,airstrips,0.06,Noun,9,8,2,airstrip,none,none,8.16,1.00,none,none,none,
airtight,airtight,1.12,Adjective,8,5,2,airtight,9.84,1.00,9.84,1.00,none,none,none,
airtime,airtime,0.25,Noun,7,5,2,airtime,11.78,1.00,11.78,1.00,none,none,none,
airwave,airwave,0.02,Noun,7,5,2,airwave,11.35,0.94,11.35,0.94,none,none,none,
airwaves,airwaves,0.53,Noun,8,6,2,airwave,none,none,11.35,0.94,none,none,none,
airway,airway,1.63,Noun,6,4,2,airway,11.16,1.00,11.16,1.00,none,none,none,
airways,airways,0.88,Noun,7,5,2,airway,none,none,11.16,1.00,none,none,none,
airworthy,airworthy,0.02,Adjective,9,6,3,airworthy,13.31,0.89,13.31,0.89,none,none,none,
airy,airy,0.31,Adjective,4,3,2,airy,10.28,0.95,10.28,0.95,none,none,none,
aisle,aisle,7.43,Noun,5,2,1,aisle,5.95,1.00,5.95,1.00,none,8.11,7.98,
aisles,aisles,0.73,Noun,6,3,1,aisle,none,none,5.95,1.00,none,8.11,7.98,
ajar,ajar,0.45,Adjective,4,4,2,ajar,9.67,0.95,9.67,0.95,none,none,none,
aka,aka,1.02,Abbreviation,3,3,2,aka,10.94,0.95,10.94,0.95,none,none,none,
akimbo,akimbo,0.08,Noun,6,6,3,akimbo,14.09,0.58,14.09,0.58,none,none,none,
akin,akin,0.27,Adjective,4,4,2,akin,12.67,0.86,12.67,0.86,none,none,none,
alabaster,alabaster,0.67,Noun,9,8,4,alabaster,13.67,0.83,13.67,0.83,none,none,none,
alacrity,alacrity,0.10,Noun,8,8,4,alacrity,15.25,0.43,15.25,0.43,none,13.23,none,
alarm,alarm,29.84,Noun,5,5,2,alarm,6.39,1.00,6.39,1.00,5.87,none,none,6.00
alarmclock,alarm clock,Err:512,Noun,10,9,3,alarmclock,5.80,1.00,5.80,1.00,none,none,none,
alarmed,alarmed,2.96,Verb,7,6,2,alarm,none,none,6.39,1.00,5.87,none,none,6.00
alarming,alarming,1.14,Adjective,8,7,3,alarming,9.78,1.00,9.78,1.00,none,none,none,
alarmist,alarmist,0.33,Noun,8,8,3,alarmist,13.12,0.94,13.12,0.94,none,none,none,
alarms,alarms,3.08,Noun,6,6,2,alarm,none,none,6.39,1.00,5.87,none,none,6.00
alarum,alarum,0.06,Noun,6,6,3,alarum,12.38,0.44,12.38,0.44,none,none,none,
alarums,alarums,0.04,Noun,7,7,3,alarum,none,none,12.38,0.44,none,none,none,
alas,alas,3.82,Interjection,4,4,2,alas,9.78,1.00,9.78,1.00,none,none,none,12.43
albacore,albacore,0.16,Noun,8,7,3,albacore,14.47,0.89,14.47,0.89,none,none,none,
albatross,albatross,0.78,Noun,9,8,3,albatross,11.64,0.78,11.64,0.78,none,10.39,none,
albatrosses,albatrosses,0.04,Noun,11,10,4,albatross,none,none,11.64,0.78,none,10.39,none,
albinism,albinism,0.04,Noun,8,9,4,albinism,14.17,0.67,14.17,0.67,none,none,none,
albino,albino,1.16,Noun,6,6,3,albino,9.79,0.95,9.79,0.95,none,none,none,
albinos,albinos,0.08,Noun,7,7,3,albino,none,none,9.79,0.95,none,none,none,
album,album,10.24,Noun,5,5,2,album,6.72,1.00,6.72,1.00,none,none,none,7.04
albumen,albumen,Err:512,Err:512,7,7,3,albumen,15.45,0.58,15.45,0.58,none,none,none,
albumin,albumin,0.08,Noun,7,8,3,albumin,14.20,0.56,14.20,0.56,none,none,none,
albums,albums,2.33,Noun,6,6,2,album,none,none,6.72,1.00,none,none,none,7.04
alcalde,alcalde,0.27,Noun,7,6,2,alcalde,11.00,0.05,11.00,0.05,none,none,none,
alchemical,alchemical,0.12,Adjective,10,9,4,alchemical,15.64,0.58,15.64,0.58,none,none,none,
alchemist,alchemist,0.27,Noun,9,8,3,alchemist,12.00,1.00,12.00,1.00,none,12.96,none,
alchemists,alchemists,0.12,Noun,10,9,3,alchemist,none,none,12.00,1.00,none,12.96,none,
alchemy,alchemy,0.22,Noun,7,6,3,alchemy,13.35,0.89,13.35,0.89,none,none,none,
alcohol,alcohol,16.57,Noun,7,7,3,alcohol,9.00,1.00,9.00,1.00,none,none,none,
alcoholic,alcoholic,6.20,Noun,9,9,4,alcoholic,9.95,1.00,9.95,1.00,11.36,none,none,
alcoholics,alcoholics,1.06,Noun,10,10,4,alcoholic,none,none,9.95,1.00,11.36,none,none,
alcoholism,alcoholism,0.84,Noun,10,11,5,alcoholism,11.60,1.00,11.60,1.00,none,none,none,
alcove,alcove,0.47,Noun,6,5,2,alcove,12.11,0.95,12.11,0.95,none,none,none,
alcoves,alcoves,0.04,Noun,7,6,2,alcove,none,none,12.11,0.95,none,none,none,
alder,alder,0.10,Noun,5,4,2,alder,14.08,0.63,14.08,0.63,none,none,none,
alderman,alderman,0.80,Noun,8,7,3,alderman,14.19,0.84,14.19,0.84,none,none,none,
aldermen,aldermen,0.02,Noun,8,7,3,alderman,none,none,14.19,0.84,none,none,none,
alders,alders,0.06,Noun,6,6,2,alder,none,none,14.08,0.63,none,none,none,
ale,ale,3.96,Noun,3,2,1,ale,10.16,1.00,10.16,1.00,none,none,10.27,
alehouse,alehouse,0.02,Noun,8,5,2,alehouse,13.76,0.94,13.76,0.94,none,none,none,
aleph,aleph,0.12,Noun,5,4,2,aleph,13.70,0.53,13.70,0.53,none,none,none,
alert,alert,20.61,Adjective,5,4,2,alert,8.90,1.00,8.90,1.00,9.14,none,none,8.12
alerted,alerted,1.78,Verb,7,6,3,alert,none,
gitextract_0oqnzvih/ ├── .gitattributes ├── .gitignore ├── LICENSE.txt ├── README.md ├── lingfeat/ │ ├── _AdvancedSemantic/ │ │ ├── OSKF.py │ │ ├── WBKF.py │ │ ├── WoKF.py │ │ ├── __init__.py │ │ └── model/ │ │ ├── __init__.py │ │ ├── enwiki100 │ │ ├── enwiki100.expElogbeta.npy │ │ ├── enwiki100.state │ │ ├── enwiki150 │ │ ├── enwiki150.expElogbeta.npy │ │ ├── enwiki150.state │ │ ├── enwiki150.state.sstats.npy │ │ ├── enwiki200 │ │ ├── enwiki200.expElogbeta.npy │ │ ├── enwiki200.state │ │ ├── enwiki200.state.sstats.npy │ │ ├── enwiki50 │ │ ├── enwiki50.expElogbeta.npy │ │ ├── enwiki50.state │ │ ├── onestop100 │ │ ├── onestop100.expElogbeta.npy │ │ ├── onestop100.state │ │ ├── onestop150 │ │ ├── onestop150.expElogbeta.npy │ │ ├── onestop150.state │ │ ├── onestop200 │ │ ├── onestop200.expElogbeta.npy │ │ ├── onestop200.state │ │ ├── onestop50 │ │ ├── onestop50.expElogbeta.npy │ │ ├── onestop50.state │ │ ├── weebit100 │ │ ├── weebit100.expElogbeta.npy │ │ ├── weebit100.state │ │ ├── weebit150 │ │ ├── weebit150.expElogbeta.npy │ │ ├── weebit150.state │ │ ├── weebit200 │ │ ├── weebit200.expElogbeta.npy │ │ ├── weebit200.state │ │ ├── weebit50 │ │ ├── weebit50.expElogbeta.npy │ │ └── weebit50.state │ ├── _Discourse/ │ │ ├── EnDF.py │ │ ├── EnGF.py │ │ └── __init__.py │ ├── _LexicoSemantic/ │ │ ├── PsyF.py │ │ ├── TTRF.py │ │ ├── VarF.py │ │ ├── WorF.py │ │ ├── __init__.py │ │ └── resources/ │ │ ├── AoAKuperman.csv │ │ └── SUBTLEXus.csv │ ├── _ShallowTraditional/ │ │ ├── ShaF.py │ │ ├── TraF.py │ │ └── __init__.py │ ├── _Syntactic/ │ │ ├── POSF.py │ │ ├── PhrF.py │ │ ├── TrSF.py │ │ └── __init__.py │ ├── __init__.py │ ├── extractor.py │ └── utils.py ├── requirements.txt ├── setup.cfg ├── setup.py └── test-lingfeat.py
SYMBOL INDEX (60 symbols across 16 files)
FILE: lingfeat/_AdvancedSemantic/OSKF.py
function richness (line 17) | def richness(probability_list):
function clarity (line 24) | def clarity(probability_list):
function noise (line 34) | def noise(probability_list):
function get_probability_lists (line 47) | def get_probability_lists(token_list, dir_path):
function retrieve (line 72) | def retrieve(token_list, dir_path):
FILE: lingfeat/_AdvancedSemantic/WBKF.py
function richness (line 17) | def richness(probability_list):
function clarity (line 24) | def clarity(probability_list):
function noise (line 34) | def noise(probability_list):
function get_probability_lists (line 47) | def get_probability_lists(token_list, dir_path):
function retrieve (line 72) | def retrieve(token_list, dir_path):
FILE: lingfeat/_AdvancedSemantic/WoKF.py
function richness (line 17) | def richness(probability_list):
function clarity (line 24) | def clarity(probability_list):
function noise (line 34) | def noise(probability_list):
function get_probability_lists (line 47) | def get_probability_lists(token_list, dir_path):
function retrieve (line 72) | def retrieve(token_list, dir_path):
FILE: lingfeat/_Discourse/EnDF.py
function retrieve (line 18) | def retrieve(NLP_doc, n_sent, n_token):
FILE: lingfeat/_Discourse/EnGF.py
function dependency_mapping (line 41) | def dependency_mapping(dep):
class EntityGrid (line 62) | class EntityGrid:
method __init__ (line 64) | def __init__(self, NLP_doc, n_sent):
method retrieve (line 137) | def retrieve(self):
function weighting_syntactic_role (line 161) | def weighting_syntactic_role(entity_role):
function get_local_coherence (line 185) | def get_local_coherence(EntityGrid):
FILE: lingfeat/_LexicoSemantic/PsyF.py
function retrieve (line 22) | def retrieve(token_list, n_token, n_sent, dir_path):
FILE: lingfeat/_LexicoSemantic/TTRF.py
function retrieve (line 21) | def retrieve(n_token,token_list):
FILE: lingfeat/_LexicoSemantic/VarF.py
function retrieve (line 22) | def retrieve(NLP_doc):
FILE: lingfeat/_LexicoSemantic/WorF.py
function retrieve (line 21) | def retrieve(token_list, n_token, n_sent, dir_path):
FILE: lingfeat/_ShallowTraditional/ShaF.py
function retrieve (line 21) | def retrieve(origin_doc, token_list, n_token, n_sent):
FILE: lingfeat/_ShallowTraditional/TraF.py
class formulas (line 23) | class formulas:
method __init__ (line 24) | def __init__(self, origin_doc, sent_token_list, n_sent, n_token):
method automated_readability_index (line 55) | def automated_readability_index(self):
method fog_count (line 60) | def fog_count(self):
method flesch_grade_level (line 72) | def flesch_grade_level(self):
method smog_index (line 77) | def smog_index(self):
method coleman_liau_index (line 82) | def coleman_liau_index(self):
method linsear_write_formula (line 87) | def linsear_write_formula(self):
function retrieve (line 97) | def retrieve(origin_doc, sent_token_list, n_sent, n_token):
FILE: lingfeat/_Syntactic/POSF.py
function retrieve (line 20) | def retrieve(NLP_doc, n_token, n_sent):
FILE: lingfeat/_Syntactic/PhrF.py
function retrieve (line 20) | def retrieve(SuPar, sent_token_list, n_token, n_sent):
FILE: lingfeat/_Syntactic/TrSF.py
function retrieve (line 21) | def retrieve(SuPar, sent_token_list, n_token, n_sent):
FILE: lingfeat/extractor.py
function nop (line 20) | def nop(it, *a, **k):
class pass_text (line 66) | class pass_text:
method __init__ (line 78) | def __init__(self, text:str):
method preprocess (line 105) | def preprocess(self, short=False, see_token=False, see_sent_token=False):
method WoKF_ (line 172) | def WoKF_(self):
method WBKF_ (line 200) | def WBKF_(self):
method OSKF_ (line 228) | def OSKF_(self):
method EnDF_ (line 246) | def EnDF_(self):
method EnGF_ (line 280) | def EnGF_(self):
method PhrF_ (line 354) | def PhrF_(self):
method TrSF_ (line 372) | def TrSF_(self):
method POSF_ (line 445) | def POSF_(self):
method TTRF_ (line 462) | def TTRF_(self):
method VarF_ (line 489) | def VarF_(self):
method PsyF_ (line 515) | def PsyF_(self):
method WorF_ (line 557) | def WorF_(self):
method ShaF_ (line 575) | def ShaF_(self):
method TraF_ (line 593) | def TraF_(self):
FILE: lingfeat/utils.py
function division (line 15) | def division(x, y):
function nan_check (line 22) | def nan_check(result):
function count_syllables (line 28) | def count_syllables(word:str):
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"preview": "Word,Word_lowercased,FREQcount,CDcount,FREQlow,Cdlow,SUBTLWF,Lg10WF,SUBTLCD,Lg10CD\r\nthe,the,1501908,8388,1339811,8388,2"
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{
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"chars": 40,
"preview": "from lingfeat.extractor import pass_text"
},
{
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"chars": 24445,
"preview": "# -*- coding: UTF-8 -*-\n\"\"\"\nSoftware: LingFeat - Comprehensive Linguistic Features for Readability Assessment\nPage: extr"
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"path": "lingfeat/utils.py",
"chars": 724,
"preview": "\"\"\"\nSoftware: LingFeat - Comprehensive Linguistic Features for Readability Assessment\nPage: utils.py\nLicense: CC-BY-SA 4"
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{
"path": "requirements.txt",
"chars": 186,
"preview": "spacy>=3.0.0,<4.0.0\nhttps://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.0.0/en_core_web_sm-3.0."
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{
"path": "setup.cfg",
"chars": 65,
"preview": "[metadata]\ndescription-file = README.md\nlicense_files=LICENSE.txt"
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{
"path": "setup.py",
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"preview": "\"\"\"\nSoftware: LingFeat - Comprehensive Linguistic Features for Readability Assessment\nPage: setup.py\nLicense: CC-BY-SA 4"
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{
"path": "test-lingfeat.py",
"chars": 2244,
"preview": "from lingfeat import extractor\n\ntext = \"TAEAN, South Chungcheong Province -- Just before sunup, Lee Young-ho, a seasoned"
}
]
// ... and 24 more files (download for full content)
About this extraction
This page contains the full source code of the brucewlee/lingfeat GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 71 files (118.1 MB), approximately 2.1M tokens, and a symbol index with 60 extracted functions, classes, methods, constants, and types. 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.