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    Measuring Text Difficulty Using Parse-Tree Frequency

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    2017-parse tree frequency.pdf
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    Final Accepted Manuscript
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    Author
    Kauchak, David
    Leroy, Gondy
    Hogue, Alan
    Affiliation
    Univ Arizona, Dept Management Informat Syst, Eller Coll Management
    Univ Arizona, Dept Linguist
    Issue Date
    2017-09
    
    Metadata
    Show full item record
    Publisher
    WILEY
    Citation
    Kauchak, D., Leroy, G., & Hogue, A. (2017). Measuring text difficulty using parse‐tree frequency. Journal of the Association for Information Science and Technology, 68(9), 2088-2100.
    Journal
    JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY
    Rights
    ©2017 ASIS&T
    Collection Information
    This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
    Abstract
    Text simplification often relies on dated, unproven readability formulas. As an alternative and motivated by the success of term familiarity, we test a complementary measure: grammar familiarity. Grammar familiarity is measured as the frequency of the 3rd level sentence parse tree and is useful for evaluating individual sentences. We created a database of 140K unique 3rd level parse structures by parsing and binning all 5.4M sentences in English Wikipedia. We then calculated the grammar frequencies across the corpus and created 11 frequency bins. We evaluate the measure with a user study and corpus analysis. For the user study, we selected 20 sentences randomly from each bin, controlling for sentence length and term frequency, and recruited 30 readers per sentence (N = 6,600) on Amazon Mechanical Turk. We measured actual difficulty (comprehension) using a Cloze test, perceived difficulty using a 5-point Likert scale, and time taken. Sentences with more frequent grammatical structures, even with very different surface presentations, were easier to understand, perceived as easier, and took less time to read. Outcomes from readability formulas correlated with perceived but not with actual difficulty. Our corpus analysis shows how the metric can be used to understand grammar regularity in a broad range of corpora.
    Note
    12 month embargo; published online 20 June 2017
    ISSN
    23301635
    DOI
    10.1002/asi.2017.68.issue-9
    Version
    Final accepted manuscript
    Sponsors
    National Library of Medicine of the National Institutes of Health [R01LM011975]
    Additional Links
    http://doi.wiley.com/10.1002/asi.2017.68.issue-9
    ae974a485f413a2113503eed53cd6c53
    10.1002/asi.2017.68.issue-9
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