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NegAIT: A new parser for medical text simplification using morphological, sentential and double negation
Author
Mukherjee, ParthaLeroy, Gondy
Kauchak, David
Rajanarayanan, Srinidhi
Romero Diaz, Damian Y
Yuan, Nicole P
Pritchard, T Gail
Colina, Sonia
Affiliation
Univ ArizonaIssue Date
2017-01-01
Metadata
Show full item recordPublisher
ACADEMIC PRESS INC ELSEVIER SCIENCECitation
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.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
1532-0480PubMed ID
28342946Version
Final accepted manuscriptSponsors
National Library of Medicine of the National Institutes of Health [R01 LM011975]ae974a485f413a2113503eed53cd6c53
10.1016/j.jbi.2017.03.014
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