Semantic Parsing with Structured SVM Ensemble Classification Models

We present a learning framework for structured support vector models in which boosting and bagging methods are used to construct ensemble models. We also propose 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 advantages in comparison with the original large margin methods.

• Type: Conference
• Year: 2006
• Where: ACL
• Authors: Minh Le Nguyen, Akira Shimazu, Xuan Hieu Phan
• Keywords: ACL 2006, ACL 2007, Language Parsing Problems, Support Vector Models, Switching Model