Semantic parsing with structured SVM ensemble classification models
説明
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.
収録刊行物
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- Proceedings of the COLING/ACL on Main conference poster sessions -
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Proceedings of the COLING/ACL on Main conference poster sessions - 619-626, 2006-01-01
Association for Computational Linguistics (ACL)
