|Title||Graph Based Multi-View Learning for CDL Relation Classification|
|Publication Type||Conference Paper|
|Year of Publication||2009|
|Authors||Li, H, Matsuo, Y, Ishizuka, M|
|Conference Name||International Conference on Semantic Computing|
|Publisher||IEEE Computer Society|
|Conference Location||Los Alamitos, CA, USA|
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.