Parsing citations in biomedical articles using conditional random fields

Zhang, Qing; Cao, Yong-Gang; Yu, Hong

Affiliations

¹University of Wisconsin-Milwaukee, Milwaukee, WI, 53211, USA

Abstract and keywords

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.

Keywords: Natural language processing; Information extraction; Citation parsing; Citation indexing; Conditional random fields; Machine learning; Biomedical text mining

Links

DOI: 10.1016/j.compbiomed.2011.02.005

PubMed: 21419403

WoS: 000289917200003

History

Received: 2009-09-1

Accepted: 2011-02-9

Online: 2011-03-17

Cited Works (2)

Year	Entry
1976	Voos H, Dagaev KS. Are all citations equal? or, did we op. cit. your idem? J Acad Librariansh. 1976;1(6):19-21.
1978	Herlach G. Can retrieval of information from citation indexes be simplified? multiple mention of a reference as a characteristic of the link between cited and citing article. J Am Soc Inform Sci. 1978;29(6):308-310.