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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
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 ( corpus. The experiment results validate its effectiveness.
graph based model, multi-view learning, CDL, relation classification, semi-supervised learning;

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.
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