Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/15184
Title: Keyphrase extraction based on topic relevance and term association
Authors: Li, D
Li, S
Li, W 
Gu, C
Li, Y
Keywords: Betweenness
Keyphrase extraction
Term association
Topic relevance
Issue Date: 2010
Source: Journal of information and computational science, 2010, v. 7, no. 1, p. 293-299 How to cite?
Journal: Journal of Information and Computational Science 
Abstract: Keyphrases are concise representation of documents and usually are extracted directly from the original text. This paper proposes a novel approach to extract keyphrases. This method proposes two metrics, named topic relevance and term association respectively, for determining whether a term is a keyphrase. Using Wikipedia knowledge and betweenness computation, we compute these two metrics and combine them to extract important phrases from the text. Experimental results show the effectiveness of the proposed approach for keyphrases extaction.
URI: http://hdl.handle.net/10397/15184
ISSN: 1548-7741
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