A composite kernel approach for detecting interactive segments in Chinese topic documents

Yung Chun Chang, Chien Chin Chen, Wen Lian Hsu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Discovering the interactions between persons mentioned in a set of topic documents can help readers construct the background of a topic and facilitate comprehension. In this paper, we propose a rich interactive tree structure to represent syntactic, content, and semantic information in text. We also present a composite kernel classification method that integrates the tree structure with a bigram kernel to identify text segments that mention person interactions in topic documents. Empirical evaluations demonstrate that the proposed tree structure and bigram kernel are effective and the composite kernel approach outperforms well-known relation extraction and PPI methods.

Original languageEnglish
Title of host publicationInformation Retrieval Technology - 9th Asia Information Retrieval Societies Conference, AIRS 2013, Proceedings
Pages215-226
Number of pages12
Volume8281 LNCS
DOIs
Publication statusPublished - 2013
Externally publishedYes
Event9th Asia Information Retrieval Societies Conference on Information Retrieval Technology, AIRS 2013 - Singapore, Singapore
Duration: Dec 9 2013Dec 11 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8281 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference9th Asia Information Retrieval Societies Conference on Information Retrieval Technology, AIRS 2013
CountrySingapore
CitySingapore
Period12/9/1312/11/13

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Keywords

  • Composite Kernel
  • Interaction Detection
  • Rich Interactive Tree
  • Topic Mining

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Chang, Y. C., Chen, C. C., & Hsu, W. L. (2013). A composite kernel approach for detecting interactive segments in Chinese topic documents. In Information Retrieval Technology - 9th Asia Information Retrieval Societies Conference, AIRS 2013, Proceedings (Vol. 8281 LNCS, pp. 215-226). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8281 LNCS). https://doi.org/10.1007/978-3-642-45068-6_19