Issue No. 01 - January (2009 vol. 21)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2008.95
Deyu Zhou , The University of Reading, Reading
Yulan He , The University of Reading, Reading
In this paper, we discuss how discriminative training can be applied to the Hidden Vector State (HVS) model in different task domains. The HVS model is a discrete Hidden Markov Model (HMM) in which each HMM state represents the state of a push-down automaton with a finite stack size. In previous applications, Maximum Likelihood estimation (MLE) is used to derive the parameters of the HVS model. However, MLE makes a number of assumptions and unfortunately some of these assumptions do not hold. Discriminative training, without making such assumptions, can improve the performance of the HVS model. Experiments have been conducted in two domains: the travel domain for the semantic parsing task using the DARPA Communicator data and the ATIS data, and the bioinformatics domain for the information extraction task using the GENIA corpus. The results demonstrate modest improvements of the performance of the HVS model using discriminative training. In the travel domain, discriminative training of the HVS model gives a relative error reduction rate of 31% in F-measure when compared with MLE on the DARPA Communicator data and 9% on the ATIS data. In the bioinformatics domain, a relative error reduction rate of 4% in F-measure is achieved on the GENIA corpus.
Language parsing and understanding, Machine learning, Parameter learning
D. Zhou and Y. He, "Discriminative Training of the Hidden Vector State Model for Semantic Parsing," in IEEE Transactions on Knowledge & Data Engineering, vol. 21, no. , pp. 66-77, 2008.