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2009 IEEE International Conference on Semantic Computing (2009)
Berkeley, CA, USA
Sept. 14, 2009 to Sept. 16, 2009
ISBN: 978-0-7695-3800-6
pp: 473-480
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.
INDEX TERMS
graph based model, multi-view learning, CDL, relation classification, semi-supervised learning;
CITATION

Y. Matsuo, M. Ishizuka and H. Li, "Graph Based Multi-View Learning for CDL Relation Classification," 2009 IEEE International Conference on Semantic Computing(ICSC), Berkeley, CA, USA, 2009, pp. 473-480.
doi:10.1109/ICSC.2009.97
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