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2009
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A ranking approach to keyphrase extraction

9 years 8 months ago
A ranking approach to keyphrase extraction
This paper addresses the issue of automatically extracting keyphrases from document. Previously, this problem was formalized as classification and learning methods for classification were utilized. This paper points out that it is more essential to cast the keyphrase extraction problem as ranking and employ a learning to rank method to perform the task. As example, it employs Ranking SVM, a state-of-art method of learning to rank, in keyphrase extraction. Experiments conducted on three datasets show that Ranking SVM significantly outperforms the baseline methods of classification, indicating that it is better to exploit learning to rank techniques in keyphrase extraction. Key words: Keyphrase extraction, Learning to rank, Ranking SVM
Xin Jiang, Yunhua Hu, Hang Li
Added 28 May 2010
Updated 28 May 2010
Type Conference
Year 2009
Where SIGIR
Authors Xin Jiang, Yunhua Hu, Hang Li
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