title: Automatically classifying the role of citations in biomedical articles.
abstract: Citations are widely used in scientific literature. The traditional model of referencing considers all citations to be the same; however, semantically, citations play different roles. By studying the context in which citations appear, it is possible to determine the role that they play. Here, we report on the development of an eight-category classification scheme, annotation using that scheme, and development and evaluation of supervised machine-learning classifiers using the annotated data. We annotated 1,710 sentences using the annotation schema and our trained classifier obtained an average F1-score of 76.5%. The classifier is available for free as a Java API from http://citation.askhermes.org.
authors.affiliation: University of Wisconsin-Milwaukee, Milwaukee, WI.
authors.forename: Shashank
authors.initials: S
authors.lastname: Agarwal
authors.forename: Lisha
authors.initials: L
authors.lastname: Choubey
authors.forename: Hong
authors.initials: H
authors.lastname: Yu
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D000069553:Supervised Machine Learning