tailieunhanh - Báo cáo khoa học: "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. | Semantic parsing with Structured SVM Ensemble Classification Models Le-Minh Nguyen Akira Shimazu and Xuan-Hieu Phan Japan Advanced Institute of Science and Technology JAIST Asahidai 1-1 Nomi Ishikawa 923-1292 Japan nguyenml shimazu hieuxuan @ Abstract 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. 1 Introduction Natural language semantic parsing is an interesting problem in NLP Manning and Schutze 1999 as it would very likely be part of any interesting NLP applications Allen 1995 . For example the necessary of semantic parsing for most of NLP application and the ability to map natural language to a formal query or command language is critical for developing more user-friendly interfaces. Recent approaches have focused on using structured prediction for dealing with syntactic parsing B. Taskar et. al. 2004 and text chunking problems Lafferty et al 2001 . For semantic parsing Zettlemoyer and Collins 2005 proposed a method for mapping a NL sentence to its logical form by structured classification using a log-linear model that represents a distribution over syntactic and semantic analyses conditioned on the input sentence. Taskar et al B. Taskar et. al. 2004 present a discriminative approach to pars ing inspired by the large-margin criterion underlying support vector machines in which the loss function is factorized analogous to the decoding process. Tsochantaridis et al Tsochantaridis et al. 2004 propose a large-margin .