Extraction of key-phrases from biomedical full-text with supervised learning techniques

Yanliang Qi, Artun I. Yagci, Min Song

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Key-phrase extraction plays useful a role in the research area of Information Systems (IS) such as digital libraries. Short metadata like key phrases could be beneficial for searchers to understand the concepts of documents' concept. This paper evaluates the effectiveness of different supervised learning techniques on biomedical full-text: Naïve Bayes, linear regression, SVMs (reg1/2), all of which could be embedded inside an IS for document search. We use these techniques to extract key phrases from PubMed. We evaluate the performance of these systems using the well-established holdout validation method. The contributions of the paper are comparison among different classifier techniques, and a comparison of performance differences between full-text and abstract. We conducted experiments and found that SVMreg-1 improves the performance of key-phrase extraction from full-text while Naïve Bayes improves from the abstracts. These techniques should be considered for use in information system search functionality. Additional research issues also are identified.

Original languageEnglish
Title of host publication15th Americas Conference on Information Systems 2009, AMCIS 2009
Pages2992-3000
Number of pages9
Publication statusPublished - 2009 Dec 1
Event15th Americas Conference on Information Systems 2009, AMCIS 2009 - San Francisco, CA, United States
Duration: 2009 Aug 62009 Aug 9

Publication series

Name15th Americas Conference on Information Systems 2009, AMCIS 2009
Volume5

Other

Other15th Americas Conference on Information Systems 2009, AMCIS 2009
CountryUnited States
CitySan Francisco, CA
Period09/8/609/8/9

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Computer Networks and Communications
  • Information Systems
  • Library and Information Sciences

Fingerprint Dive into the research topics of 'Extraction of key-phrases from biomedical full-text with supervised learning techniques'. Together they form a unique fingerprint.

  • Cite this

    Qi, Y., Yagci, A. I., & Song, M. (2009). Extraction of key-phrases from biomedical full-text with supervised learning techniques. In 15th Americas Conference on Information Systems 2009, AMCIS 2009 (pp. 2992-3000). (15th Americas Conference on Information Systems 2009, AMCIS 2009; Vol. 5).