Partha Mukherjee|Gondy Leroy|David Kauchak|Srinidhi Rajanarayanan|Damian Y. Romero Diaz|Nicole P. Yuan|T. Gail Pritchard|Sonia Colina
NegAIT: A new parser for medical text simplification using morphological, sentential and double negation
University of Arizona|University of Arizona|Pomona College|University of Arizona|University of Arizona|University of Arizona|University of Arizona|University of Arizona
many different text features influence text readability content comprehension negation commonly suggested one feature generalpurpose tools exist discover negation studies impact negation text readability rare paper introduce new negation parser negait detecting morphological sentential double negation evaluated parser using human annotated gold standard containing 500 wikipedia sentences achieved 95 89 67 precision 100 80 67 recall respectively also investigate two applications new negation parser first performed corpus statistics study demonstrate different negation usage easy difficult text negation usage compared six corpora patient blogs 4k sentences cochrane reviews 91k sentences pubmed abstracts 20k sentences clinical trial texts 48k sentences english simple english wikipedia articles different medical topics 60k 6k sentences difficult text contained least negation however comparing negation types difficult texts ie cochrane pubmed english wikipedia clinical trials contained significantly p001 morphological negations second conducted predictive analytics study show importance negation distinguishing easy difficulty text five binary classifiers nave bayes svm decision tree logistic regression linear regression trained using negation information classifiers achieved better performance majority baseline nave bayes classifier achieved highest accuracy 77 9 higher majority baseline