The nature of statistical learning theory
The nature of statistical learning theory
Exploiting generative models in discriminative classifiers
Proceedings of the 1998 conference on Advances in neural information processing systems II
Interleaving syntax and semantics in an efficient bottom-up parser
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Recent improvements in the CMU spoken language understanding system
HLT '94 Proceedings of the workshop on Human Language Technology
Large Margin Methods for Structured and Interdependent Output Variables
The Journal of Machine Learning Research
Hybrid Generative-Discriminative Visual Categorization
International Journal of Computer Vision
Learning conditional random fields from unaligned data for natural language understanding
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Biomedical events extraction using the hidden vector state model
Artificial Intelligence in Medicine
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We propose a hybrid generative/discriminative framework for semantic parsing which combines the hidden vector state (HVS) model and the hidden Markov support vector machines (HM-SVMs). The HVS model is an extension of the basic discrete Markov model in which context is encoded as a stack-oriented state vector. The HM-SVMs combine the advantages of the hidden Markov models and the support vector machines. By employing a modified K-means clustering method, a small set of most representative sentences can be automatically selected from an un-annotated corpus. These sentences together with their abstract annotations are used to train an HVS model which could be subsequently applied on the whole corpus to generate semantic parsing results. The most confident semantic parsing results are selected to generate a fully-annotated corpus which is used to train the HM-SVMs. The proposed framework has been tested on the DARPA Communicator Data. Experimental results show that an improvement over the baseline HVS parser has been observed using the hybrid framework. When compared with the HM-SVMs trained from the fully-annotated corpus, the hybrid framework gave a comparable performance with only a small set of lightly annotated sentences.