Presentation 2011-12-16
A Study on Efficient Searching Top-k Semantic Similar Sentences
Yanhui GU, Zhenglu YANG, Masaru KITSUREGAWA,
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Abstract(in English) Semantic similarity measure between sentences is an important issue in many applications, such as, text mining, Web page retrieval, dialogue systems, and so forth. Although it has been explored for several decades, most of these studies focus on how to improve the effectiveness of the problem. In this paper, we address the efficiency issue, i.e., for a given sentence collection, how to efficiently discover the top-k most semantic similar sentences to the query. It is a very important issue for real applications while existing state-of-the-art strategies cannot satisfy the performance requirement of the users. We introduce a general framework to tackle the issue, in which several efficient strategies are proposed. Extensive experimental evaluations demonstrate that our approach outperforms the state-of-the-art methods.
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Keyword(in English) semantic similarity / query aggregation / Top-k
Paper # DE2011-42
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Committee DE
Conference Date 2011/12/9(1days)
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Language ENG
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) A Study on Efficient Searching Top-k Semantic Similar Sentences
Sub Title (in English)
Keyword(1) semantic similarity
Keyword(2) query aggregation
Keyword(3) Top-k
1st Author's Name Yanhui GU
1st Author's Affiliation Institute of Industrial Science, The University of Tokyo()
2nd Author's Name Zhenglu YANG
2nd Author's Affiliation Institute of Industrial Science, The University of Tokyo
3rd Author's Name Masaru KITSUREGAWA
3rd Author's Affiliation Institute of Industrial Science, The University of Tokyo
Date 2011-12-16
Paper # DE2011-42
Volume (vol) vol.111
Number (no) 361
Page pp.pp.-
#Pages 6
Date of Issue