Paper: Boosting-Based Parse Reranking With Subtree Features

ACL ID P05-1024
Title Boosting-Based Parse Reranking With Subtree Features
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2005

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