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Modeling commonality among related classes in relation extraction

Published:17 July 2006Publication History

ABSTRACT

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 linear discriminative function is determined in a top-down way using a perceptron algorithm with the lower-level weight vector derived from the upper-level weight vector. As the upper-level class normally has much more positive training examples than the lower-level class, the corresponding linear discriminative function can be determined more reliably. The upper-level discriminative function then can effectively guide the discriminative function learning in the lower-level, which otherwise might suffer from limited training data. Evaluation on the ACE RDC 2003 corpus shows that the hierarchical strategy much improves the performance by 5.6 and 5.1 in F-measure on least- and medium- frequent relations respectively. It also shows that our system outperforms the previous best-reported system by 2.7 in F-measure on the 24 subtypes using the same feature set.

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  1. Modeling commonality among related classes in relation extraction

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        • Published in

          cover image DL Hosted proceedings
          ACL-44: Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
          July 2006
          1214 pages

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          Association for Computational Linguistics

          United States

          Publication History

          • Published: 17 July 2006

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          Overall Acceptance Rate85of443submissions,19%

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