Parsing citations in biomedical articles using conditional random fields.
Computers in Biology and Medicine（2011）
摘要
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.
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关键词
citation parsing,biomedical full-text article,rhetorical structure,biomedical text mining,important role,biomedical article,supervised machine-learning algorithms conditional,text mining system,citation parser,random fields,citation indexing,parsing citation,central article,conditional random field,semantic content,information extraction,natural language processing,conditional random fields,machine learning,html format open-access
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