Presentation 2005/7/15
Identifying a cross-document relation between sentences
Yasunari Miyabe, Hiroya Takamura, Manabu Okumura,
PDF Download Page PDF download Page Link
Abstract(in Japanese) (See Japanese page)
Abstract(in English) We propose a machine learning based method that identifies an equivalence relation between sentences in different newspaper articles on a topic. We showed that our method, which divided the corpus into several classes by sentence similarity and learned a classifier, yielded a superior result than without dividing it. In addition, compared with the number of total sentence pairs, the number of sentence pairs in an equivalence relation is too small in a relatively less similar class. Therefore, the classifier sometimes cannot identify equivalence relations. To solve this problem, we use "relations similar to equivalence" that describe a same content more briefly or in more detail in different newspaper articles. We also propose a two-stage method that first identifies a coarse class that includes both an equivalence relation and "relations similar to equivalence", and then identifies an equivalence relation from a coarse class. We showed that high accuracy was yielded by combining these two methods.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) sentence similarity / cross-document structure theory / discourse structure analysis
Paper # NLC2005-6
Date of Issue

Conference Information
Committee NLC
Conference Date 2005/7/15(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 Natural Language Understanding and Models of Communication (NLC)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Identifying a cross-document relation between sentences
Sub Title (in English)
Keyword(1) sentence similarity
Keyword(2) cross-document structure theory
Keyword(3) discourse structure analysis
1st Author's Name Yasunari Miyabe
1st Author's Affiliation Department of Computational Intelligence and Systems Science, Interdiciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology()
2nd Author's Name Hiroya Takamura
2nd Author's Affiliation Precision and Intelligence Laboratory, Tokyo Institute of Technology
3rd Author's Name Manabu Okumura
3rd Author's Affiliation Precision and Intelligence Laboratory, Tokyo Institute of Technology
Date 2005/7/15
Paper # NLC2005-6
Volume (vol) vol.105
Number (no) 203
Page pp.pp.-
#Pages 8
Date of Issue