Skip to content
/ lingfeat Public

[EMNLP 2021] LingFeat - A Comprehensive Linguistic Features Extraction ToolKit for Readability Assessment

License

CC-BY-SA-4.0 license
120 stars 16 forks Branches Tags Activity
Star
Notifications You must be signed in to change notification settings

brucewlee/lingfeat

BranchesTags

Repository files navigation

CC BY-SA 4.0 spaCy PyPI Python 3.5+ LingFeat

LingFeat - Comprehensive Linguistic Features Extraction Tool for Readability Assessment and Text Simplification

Migration Notice - 2023-03-06

LingFeat is now maintained in a new repository named 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 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 to install LingFeat.

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.

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.

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.

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.

"""
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).

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.

About

[EMNLP 2021] LingFeat - A Comprehensive Linguistic Features Extraction ToolKit for Readability Assessment

Topics

nlp natural-language-processing text-classification discourse spacy feature-extraction lexical-analysis readability-metrics flesch-kincaid semantic-analysis syntactic-analysis linguistic-analysis text-simplification readability-scores

Resources

Readme

License

CC-BY-SA-4.0 license
Activity

Stars

120 stars

Watchers

1 watching

Forks

16 forks
Report repository

Releases

2 tags

Packages

No packages published

Languages