title: Parsing citations in biomedical articles using conditional random fields.
abstract: Citations are used ubiquitously in biomedical full-text articles and play an important role for representing both the rhetorical structure and the semantic content of the articles. As a result, text mining systems will significantly benefit from a tool that automatically extracts the content of a citation. In this study, we applied the supervised machine-learning algorithms Conditional Random Fields (CRFs) to automatically parse a citation into its fields (e.g., Author, Title, Journal, and Year). With a subset of html format open-access PubMed Central articles, we report an overall 97.95% F1-score. The citation parser can be accessed at: http://www.cs.uwm.edu/∼qing/projects/cithit/index.html.
authors.affiliation: University of Wisconsin-Milwaukee, Milwaukee, WI 53211, USA.
authors.forename: Qing
authors.initials: Q
authors.lastname: Zhang
authors.forename: Yong-Gang
authors.initials: YG
authors.lastname: Cao
authors.forename: Hong
authors.initials: H
authors.lastname: Yu
mesh_terms: D000465:Algorithms; D039781:PubMed; D012984:Software