Welcome to the InfoSci Platform
Reference Hub14
Combining Supervised Learning Techniques to Key-Phrase Extraction for Biomedical Full-Text

Combining Supervised Learning Techniques to Key-Phrase Extraction for Biomedical Full-Text

Yanliang Qi, Min Song, Suk-Chung Yoon, Lori deVersterre
Copyright: © 2011 |Volume: 7 |Issue: 1 |Pages: 12
ISSN: 1548-3657|EISSN: 1548-3665|EISBN13: 9781613507933|DOI: 10.4018/jiit.2011010103
Cite Article Cite Article

MLA

Qi, Yanliang, et al. "Combining Supervised Learning Techniques to Key-Phrase Extraction for Biomedical Full-Text." IJIIT vol.7, no.1 2011: pp.33-44. http://doi.org/10.4018/jiit.2011010103

APA

Qi, Y., Song, M., Yoon, S., & deVersterre, L. (2011). Combining Supervised Learning Techniques to Key-Phrase Extraction for Biomedical Full-Text. International Journal of Intelligent Information Technologies (IJIIT), 7(1), 33-44. http://doi.org/10.4018/jiit.2011010103

Chicago

Qi, Yanliang, et al. "Combining Supervised Learning Techniques to Key-Phrase Extraction for Biomedical Full-Text," International Journal of Intelligent Information Technologies (IJIIT) 7, no.1: 33-44. http://doi.org/10.4018/jiit.2011010103

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

Key-phrase extraction plays a useful a role in research areas of Information Systems (IS) like digital libraries. Short metadata like key phrases are beneficial for searchers to understand the concepts found in the documents. This paper evaluates the effectiveness of different supervised learning techniques on biomedical full-text: Sequential Minimal Optimization (SMO) and K-Nearest Neighbor, both of which could be embedded inside an information system for document search. The authors use these techniques to extract key phrases from PubMed and evaluate the performance of these systems using the holdout validation method. This paper compares different classifier techniques and performance differences between the full-text and it’s abstract. Compared with the authors’ previous work, which investigated the performance of Naïve Bayes, Linear Regression and SVM(reg1/2), this paper finds that SVMreg-1 performs best in key-phrase extraction for full-text, whereas Naïve Bayes performs best for abstracts. These techniques should be considered for use in information system search functionality. Additional research issues also are identified.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.