Presentation 2012-08-01
Exploration on Efficient Similar Sentences Extraction
Yanhui GU, Zhenglu YANG, Miyuki NAKANO, Masaru KITSUREGAWA,
PDF Download Page PDF download Page Link
Abstract(in Japanese) (See Japanese page)
Abstract(in English) Semantic similarity measure between sentences is an essential issue for many applications, such as natural language processing, Web page retrieval, question-answer model, and so forth. Although there are a few studies exploring on this issue, most of them 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 semantic similar sentences to a query. The issue is very important for real applications because the data becomes huge and the existing state-of-the-art strategies cannot satisfy the users' performance requirement. We propose efficient strategies to tackle such problem based on a general framework. Extensive experimental evaluations conducted on two real datasets demonstrate that the efficiency of our proposal outperforms the state-of-the-art approach.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) semantic similarity / query aggregation / top-k
Paper # DE2012-19
Date of Issue

Conference Information
Committee DE
Conference Date 2012/7/25(1days)
Place (in Japanese) (See Japanese page)
Place (in English)
Topics (in Japanese) (See Japanese page)
Topics (in English)
Chair
Vice Chair
Secretary
Assistant

Paper Information
Registration To Data Engineering (DE)
Language ENG
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Exploration on Efficient Similar Sentences Extraction
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 Miyuki NAKANO
3rd Author's Affiliation Institute of Industrial Science, the University of Tokyo
4th Author's Name Masaru KITSUREGAWA
4th Author's Affiliation Institute of Industrial Science, the University of Tokyo
Date 2012-08-01
Paper # DE2012-19
Volume (vol) vol.112
Number (no) 172
Page pp.pp.-
#Pages 6
Date of Issue