Boosting-based parse reranking with subtree features

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. © 2005 Association for Computational Linguistics.