Measure the readability of a given text using surface characteristics
An implementation of traditional readability measures based on simple 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. The supported languages are English, German, and Dutch. Adding support for a new language involves the addition of heuristics for the aforementioned syllabification and word type recognition; see langdata.py.
NB: all readability formulas were developed for English, so the scales of the outcomes are only meaningful for English texts. The Dale-Chall measure uses the original word list for English, but for Dutch and German lists of frequent words are used that were not specifically selected for recognizability by school children.
$ pip install https://github.com/andreasvc/readability/tarball/master
>>> import readability >>> text = ('This is an example sentence .\n' 'Note that tokens are separated by spaces and sentences by newlines .\n') >>> results = readability.getmeasures(text, lang='en') >>> print(results['readability grades']['FleschReadingEase']) 55.95250000000002
Command line usage:
$ readability --help Simple readability measures. Usage: readability [--lang=<x>] [FILE] or: readability [--lang=<x>] --csv FILES... By default, input is read from standard input. Text should be encoded with UTF-8, one sentence per line, tokens space-separated. Options: -L, --lang=<x> Set language (available: de, nl, en). --csv Produce a table in comma separated value format on standard output given one or more filenames. --tokenizer=<x> Specify a tokenizer including options that will be given each text on stdin and should return tokenized output on stdout. Not applicable when reading from stdin.
For proper results, the text should be tokenized.
For English, I recommend “tokenizer”, cf. http://moin.delph-in.net/WeSearch/DocumentParsing
For Dutch, I recommend the tokenizer that is part of the Alpino parser: http://www.let.rug.nl/vannoord/alp/Alpino/.
ucto is a general multilingual tokenizer: http://ilk.uvt.nl/ucto
Example using ucto:
$ ucto -L en -n -s '' "CONRAD, Joseph - Lord Jim.txt" | readability [...] readability grades: Kincaid: 5.44 ARI: 6.39 Coleman-Liau: 6.91 FleschReadingEase: 85.17 GunningFogIndex: 9.86 LIX: 31.98 SMOGIndex: 9.39 RIX: 2.56 DaleChallIndex: 8.02 sentence info: characters_per_word: 4.17 syll_per_word: 1.24 words_per_sentence: 16.35 sentences_per_paragraph: 11.5 type_token_ratio: 0.09 characters: 551335 syllables: 164205 words: 132211 wordtypes: 12071 sentences: 8087 paragraphs: 703 long_words: 20670 complex_words: 10990 complex_words_dc: 29908 word usage: tobeverb: 3907 auxverb: 1630 conjunction: 4398 pronoun: 18092 preposition: 19290 nominalization: 1167 sentence beginnings: pronoun: 2578 interrogative: 217 article: 629 subordination: 120 conjunction: 236 preposition: 397
The option --csv collects readability measures for a number of texts in a table. To tokenize documents on-the-fly when using this option, use the --tokenizer option. Example with the “tokenize” tool:
$ readability --csv --tokenizer='tokenizer -L en-u8 -P -S -E "" -N' */*.txt >readabilitymeasures.csv
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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