Boosting-based parse reranking with subtree features

  • Authors:
  • Taku Kudo;Jun Suzuki;Hideki Isozaki

  • Affiliations:
  • NTT Communication Science Laboratories, Seika-cho, Soraku, Kyoto, Japan and Google Japan Inc.;NTT Communication Science Laboratories, Seika-cho, Soraku, Kyoto, Japan;NTT Communication Science Laboratories, Seika-cho, Soraku, Kyoto, Japan

  • Venue:
  • ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
  • Year:
  • 2005

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Abstract

This paper introduces a new application of boosting for parse reranking. Several parsers have been proposed that utilize the all-subtrees representation (e.g., tree kernel and data oriented parsing). This paper argues that such an all-subtrees representation is extremely redundant and a comparable accuracy can be achieved using just a small set of subtrees. We show how the boosting algorithm can be applied to the all-subtrees representation and how it selects a small and relevant feature set efficiently. Two experiments on parse reranking show that our method achieves comparable or even better performance than kernel methods and also improves the testing efficiency.