TY - GEN
T1 - Extraction of key-phrases from biomedical full-text with supervised learning techniques
AU - Qi, Yanliang
AU - Yagci, Artun I.
AU - Song, Min
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
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M3 - Conference contribution
AN - SCOPUS:79954623508
SN - 9781615675814
T3 - 15th Americas Conference on Information Systems 2009, AMCIS 2009
SP - 2992
EP - 3000
BT - 15th Americas Conference on Information Systems 2009, AMCIS 2009
T2 - 15th Americas Conference on Information Systems 2009, AMCIS 2009
Y2 - 6 August 2009 through 9 August 2009
ER -