readability 0.3.1

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.

Installation

$ pip install https://github.com/andreasvc/readability/tarball/master

Usage

From Python:

>>> 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

References

The following readability metrics are included:

1. http://en.wikipedia.org/wiki/Automated_Readability_Index

2. http://en.wikipedia.org/wiki/SMOG

3. http://en.wikipedia.org/wiki/Flesch%E2%80%93Kincaid_Grade_Level#Flesch.E2.80.93Kincaid_Grade_Level

4. http://en.wikipedia.org/wiki/Flesch%E2%80%93Kincaid_readability_test#Flesch_Reading_Ease

5. http://en.wikipedia.org/wiki/Coleman-Liau_Index

6. http://en.wikipedia.org/wiki/Gunning-Fog_Index

7. https://en.wikipedia.org/wiki/Dale%E2%80%93Chall_readability_formula

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

• T-Scan: http://languagelink.let.uu.nl/tscan

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