NegAIT: A new parser for medical text simplification using morphological, sentential and double negation.
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 (4K sentences), Cochrane reviews (91K sentences), PubMed abstracts (20K sentences), clinical trial texts (48K sentences), and English and Simple English Wikipedia articles for different medical topics (60K and 6K 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 (Naïve 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 Naïve Bayes' classifier achieved the highest accuracy at 77% (9% higher than the majority baseline).
University of Arizona, Tucson, AZ, United States. Electronic address: parthamukherjee@email.arizona.edu.
Partha
P
Mukherjee
University of Arizona, Tucson, AZ, United States.
Gondy
G
Leroy
Pomona College, Claremont, CA, United States.
David
D
Kauchak
University of Arizona, Tucson, AZ, United States.
Srinidhi
S
Rajanarayanan
University of Arizona, Tucson, AZ, United States.
Damian Y
DY
Romero Diaz
University of Arizona, Tucson, AZ, United States.
Nicole P
NP
Yuan
University of Arizona, Tucson, AZ, United States.
T Gail
TG
Pritchard
University of Arizona, Tucson, AZ, United States.
Sonia
S
Colina
D001499:Bayes Theorem; D032882:Comprehension; D066289:Data Curation; D006801:Humans; D007802:Language; D008490:Medical Informatics / Q000379:methods; D009323:Natural Language Processing; D012984:Software
Health literacy; NLP; Negation; Readability; Text simplification