Abstract
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 Zhai’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.
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Zhou, J., Xu, Q., Chen, J., Qu, W. (2009). A Multi-view Approach for Relation Extraction. In: Liu, W., Luo, X., Wang, F.L., Lei, J. (eds) Web Information Systems and Mining. WISM 2009. Lecture Notes in Computer Science, vol 5854. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05250-7_6
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DOI: https://doi.org/10.1007/978-3-642-05250-7_6
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