Hierarchical learning strategy in relation extraction using support vector machines

This paper proposes a novel hierarchical learning strategy to deal with the data sparseness problem in relation extraction by modeling the commonality among related classes. For each class in the hierarchy either manually predefined or automatically clustered, a discriminative function is determined in a top-down way. As the upper-level class normally has much more positive training examples than the lower-level class, the corresponding discriminative function can be determined more reliably and effectively, and thus guide the discriminative function learning in the lower-level, which otherwise might suffer from limited training data. In this paper, the state-of-the-art Support Vector Machines is applied as the basic classifier learning approach using the hierarchical learning strategy. Evaluation on the ACE RDC 2003 and 2004 corpora shows that the hierarchical learning strategy much improves the performance on least- and medium-frequent relations.