A Multi-view Approach for Relation Extraction

Relation extraction is an important problem in information extraction. In this paper, we explore a multi-view strategy for relation extracting task. Motivated by the fact, as in work of Jiang and Thai's [1], that combining different feature subspaces into a single view does not generate much improvement, we propose a two-stage multi-view learning approach. First, we learn two different classifiers from two different views of relation instances: sequence representation and syntactic parse tree representation, respectively. Then, a meta-learner is trained using the meta data constructed along with other contextual information to achieve a strong predictive performance, as the final classification model. The experimental results conducted on ACE 2005 corpus show that the multi-view approach outperforms each single-view one for relation extraction task.