Boosting-based parse reranking with subtree features
Taku Kudo, Jun Suzuki, Hideki Isozaki · 2005
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.