Graph based multi-view learning for CDL relation classification

Haibo Li, Yutaka Matsuo, Mitsuru Ishizuka

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

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationICSC 2009 - 2009 IEEE International Conference on Semantic Computing
Pages473-480
Number of pages8
DOIs
Publication statusPublished - 2009
Externally publishedYes
EventICSC 2009 - 2009 IEEE International Conference on Semantic Computing - Berkeley, CA
Duration: 2009 Sep 142009 Sep 16

Other

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

Keywords

  • CDL
  • Graph based model
  • Multi-view learning
  • Relation classification
  • Semi-supervised learning

ASJC Scopus subject areas

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

Fingerprint Dive into the research topics of 'Graph based multi-view learning for CDL relation classification'. Together they form a unique fingerprint.

Cite this