GRAPH BASED MULTI-VIEW LEARNING FOR SEMANTIC RELATION EXTRACTION

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. Semantic relation can be naturally represented from two views: entity pair view and context view. Then we construct a weighted complete graph for each view and a bipartite graph to combine information of different views. An instance's label score is propagated on each intra-view graph and inter-view graph. The proposed algorithm is evaluated using the Concept Description Language for Natural Language (CDL) corpus and SemEval-2007 Task 04 dataset. The experimental results validate its effectiveness.