Graph based multi-view learning for CDL relation classification

Haibo Li, Yutaka Matsuo, Mitsuru Ishizuka

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

To understand text contents better, many research efforts have been made exploring detection and classification of the semantic relation between a concept pair. As described herein, we present our study of a semantic relation classification task as a graph-based multi-view learning task: each intra-view graph is constructed with instances in the view; a node's label "score" is propagated on each intra-view graph and inter-view graph. This combination of multi-view learning and graph-based method can reduce the influence from violation of a background assumption of multi-view learning algorithms - view compatibility. The proposed algorithm is evaluated using the Concept Description Language for Natural Language (CDL.nl) corpus. The experiment results validate its effectiveness.

本文言語English
ホスト出版物のタイトルICSC 2009 - 2009 IEEE International Conference on Semantic Computing
ページ473-480
ページ数8
DOI
出版ステータスPublished - 2009
外部発表はい
イベントICSC 2009 - 2009 IEEE International Conference on Semantic Computing - Berkeley, CA
継続期間: 2009 9 142009 9 16

Other

OtherICSC 2009 - 2009 IEEE International Conference on Semantic Computing
CityBerkeley, CA
Period09/9/1409/9/16

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Software
  • Electrical and Electronic Engineering

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