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    NegAIT: A new parser for medical text simplification using morphological, sentential and double negation

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    Author
    Mukherjee, Partha
    Leroy, Gondy
    Kauchak, David
    Rajanarayanan, Srinidhi
    Romero Diaz, Damian Y
    Yuan, Nicole P
    Pritchard, T Gail
    Colina, Sonia
    Affiliation
    Univ Arizona
    Issue Date
    2017-01-01
    Keywords
    Health literacy
    NLP
    Negation
    Readability
    Text simplification
    
    Metadata
    Show full item record
    Publisher
    ACADEMIC PRESS INC ELSEVIER SCIENCE
    Citation
    Mukherjee, P., Leroy, G., Kauchak, D., Rajanarayanan, S., Diaz, D. Y. R., Yuan, N. P., ... & Colina, S. (2017). NegAIT: A new parser for medical text simplification using morphological, sentential and double negation. Journal of biomedical informatics, 69, 55-62.
    Journal
    JOURNAL OF BIOMEDICAL INFORMATICS
    Rights
    © 2017 Elsevier Inc. All rights reserved.
    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
    Many different text features influence text readability and content comprehension. Negation is commonly suggested as one such feature, but few general-purpose tools exist to discover negation and studies of the impact of negation on text readability are rare. In this paper, we introduce a new negation parser (NegAIT) for detecting morphological, sentential, and double negation. We evaluated the parser using a human annotated gold standard containing 500 Wikipedia sentences and achieved 95%, 89% and 67% precision with 100%, 80%, and 67% recall, respectively. We also investigate two applications of this new negation parser. First, we performed a corpus statistics study to demonstrate different negation usage in easy and difficult text. Negation usage was compared in six corpora: patient blogs (4 K sentences), Cochrane reviews (91 K sentences), PubMed abstracts (20 K sentences), clinical trial texts (48 K sentences), and English and Simple English Wikipedia articles for different medical topics (60 K and 6 K sentences). The most difficult text contained the least negation. However, when comparing negation types, difficult texts (i.e., Cochrane, PubMed, English Wikipedia and clinical trials) contained significantly (p < 0.01) more morphological negations. Second, we conducted a predictive analytics study to show the importance of negation in distinguishing between easy and difficulty text. Five binary classifiers (Naive Bayes, SVM, decision tree, logistic regression and linear regression) were trained using only negation information. All classifiers achieved better performance than the majority baseline. The Naive Bayes' classifier achieved the highest accuracy at 77% (9% higher than the majority baseline). (C) 2017 Elsevier Inc. All rights reserved.
    Note
    12 month embargo; available online 22 March 2017.
    ISSN
    1532-0480
    PubMed ID
    28342946
    DOI
    10.1016/j.jbi.2017.03.014
    Version
    Final accepted manuscript
    Sponsors
    National Library of Medicine of the National Institutes of Health [R01 LM011975]
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.jbi.2017.03.014
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