Measure the readability of a given text using surface characteristics
Project description
A collection of functions that measure the readability of a given body of text using surface characteristics. These measures are basically linear regressions based on the number of words, syllables, and sentences.
The functionality is modeled after the UNIX style(1) command. Compared to the implementation as part of GNU diction, this version supports UTF-8 encoded text, but expects sentence-segmented and tokenized text. The syllabification and word type recognition is based on simple heuristics and only provides a rough measure.
NB: all readability formulas were developed for English, so the scales of the outcomes are only meaningful for English texts.
Installation
$ pip install https://github.com/andreasvc/readability/tarball/master
Usage
$ readability --help Simple readability measures. Usage: readability.py [--lang=<x>] [file] By default, input is read from standard input. Text should be encoded with UTF-8, one sentence per line, tokens space-separated. -L, --lang=<x> set language (available: de, nl, en).
For proper results, the text should be tokenized. These tokenizers support Dutch and English:
ucto: http://ilk.uvt.nl/ucto
elephant: http://gmb.let.rug.nl/elephant/
Example using ucto:
$ ucto -L en -n -s '' "CONRAD, Joseph - Lord Jim.txt" | readability
[...]
readability grades:
Kincaid: 4.95
ARI: 5.78
Coleman-Liau: 6.87
FleschReadingEase: 86.18
GunningFogIndex: 9.4
LIX: 30.97
SMOGIndex: 9.2
RIX: 2.39
sentence info:
characters_per_word: 4.19
syll_per_word: 1.25
words_per_sentence: 14.92
sentences_per_paragraph: 12.6
characters: 552074
syllables: 164207
words: 131668
sentences: 8823
paragraphs: 700
long_words: 21122
complex_words: 11306
word usage:
tobeverb: 3909
auxverb: 1632
conjunction: 4413
pronoun: 18104
preposition: 19271
nominalization: 1216
sentence beginnings:
pronoun: 2593
interrogative: 215
article: 632
subordination: 124
conjunction: 240
preposition: 404
References
The following readability metrics are included:
http://en.wikipedia.org/wiki/Flesch%E2%80%93Kincaid_Grade_Level#Flesch.E2.80.93Kincaid_Grade_Level
http://en.wikipedia.org/wiki/Flesch%E2%80%93Kincaid_readability_test#Flesch_Reading_Ease
For better readability measures, consider the following:
Collins-Thompson & Callan (2004). A language modeling approach to predicting reading difficulty. In Proc. of HLT/NAACL, pp. 193-200. http://aclweb.org/anthology/N04-1025.pdf
Schwarm & Ostendorf (2005). Reading level assessment using SVM and statistical language models. Proc. of ACL, pp. 523-530. http://www.aclweb.org/anthology/P05-1065.pdf
The Lexile framework for reading. http://www.lexile.com
Coh-Metrix. http://cohmetrix.memphis.edu/
Stylene: http://www.clips.ua.ac.be/category/projects/stylene
Acknowledgments
The code is based on: https://github.com/mmautner/readability
Which in turn was based on: https://github.com/nltk/nltk_contrib/tree/master/nltk_contrib/readability
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