Paper: Semantic Parsing With Structured SVM Ensemble Classification Models

ACL ID P06-2080
Title Semantic Parsing With Structured SVM Ensemble Classification Models
Venue Annual Meeting of the Association of Computational Linguistics
Session Poster Session
Year 2006
Authors

We present a learning framework for struc- tured support vector models in which boosting and bagging methods are used to construct ensemble models. We also pro- pose a selection method which is based on a switching model among a set of outputs of individual classifiers when dealing with natural language parsing problems. The switching model uses subtrees mined from the corpus and a boosting-based algorithm to select the most appropriate output. The application of the proposed framework on the domain of semantic parsing shows ad- vantages in comparison with the original large margin methods.