title: Improving Endpoint Detection to Support Automated Systematic Reviews.
abstract: Authors of biomedical articles use comparison sentences to communicate the findings of a study, and to compare the results of the current study with earlier studies. The Claim Framework defines a comparison claim as a sentence that includes at least two entities that are being compared, and an endpoint that captures the way in which the entities are compared. Although automated methods have been developed to identify comparison sentences from the text, identifying the role that a specific noun plays (i.e. entity or endpoint) is much more difficult. Automated methods have been successful at identifying the second entity, but classification models were unable to clearly differentiate between the first entity and the endpoint. We show empirically that establishing if head noun is an amount or measure provides a statistically significant improvement that increases the endpoint precision from 0.42 to 0.56 on longer and from 0.51 to 0.58 on shorter sentences and recall from 0.64 to 0.71 on longer and from 0.69 to 0.74 on shorter sentences. The differences were not statistically significant for the second compared entity.
authors.affiliation: School of Information Sciences, University of Illinois, Champaign, IL.
authors.forename: Ana
authors.initials: A
authors.lastname: Lucic
authors.affiliation: School of Information Sciences, University of Illinois, Champaign, IL.
authors.forename: Catherine L
authors.initials: CL
authors.lastname: Blake
mesh_terms: D000818:Animals; D035843:Biomedical Research; D003920:Diabetes Mellitus; D006801:Humans; D008037:Linguistics; D015201:Meta-Analysis as Topic; D009323:Natural Language Processing; D011642:Publications / Q000639:trends; D012196:Review Literature as Topic; D060388:Support Vector Machine