Measure the readability of a given text using surface characteristics
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.
$ pip install https://github.com/andreasvc/readability/tarball/master
$ 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:
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
The following readability metrics are included:
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
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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