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Discriminative training of the hidden vector state model for semantic parsing

Zhou, Deyu and He, Yulan (2009). Discriminative training of the hidden vector state model for semantic parsing. IEEE Transactions on Knowledge and Data Engineering, 21(1) pp. 66–77.

DOI (Digital Object Identifier) Link: http://dx.doi.org/10.1109/TKDE.2008.95
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Abstract

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 by discriminating the correct hypothesis from the competing hypotheses. Experiments have been conducted in two domains: the travel domain for the semantic parsing task using the DARPA Communicator data and the Air Travel Information Services (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 percent in F-measure when compared with MLE on the DARPA Communicator data and 9 percent on the ATIS data. In the bioinformatics domain, a relative error reduction rate of 4 percent in F-measure is achieved on the GENIA corpus.

Item Type: Journal Article
Copyright Holders: 2009 IEEE
ISSN: 1041-4347
Extra Information: Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
Keywords: language parsing and understanding; machine learning; parameter learning
Academic Unit/Department: Knowledge Media Institute
Interdisciplinary Research Centre: Centre for Research in Computing (CRC)
Item ID: 23787
Depositing User: Kay Dave
Date Deposited: 20 Oct 2010 11:16
Last Modified: 19 Jun 2014 17:17
URI: http://oro.open.ac.uk/id/eprint/23787
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