Discriminative training of the hidden vector state model for semantic parsing


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.

Publication DOI: https://doi.org/10.1109/TKDE.2008.95
Divisions: College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
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Uncontrolled Keywords: language parsing and understanding,machine learning ,parameter learning
Publication ISSN: 1558-2191
Last Modified: 27 Jun 2024 08:10
Date Deposited: 24 Jan 2013 13:48
Full Text Link: http://ieeexplo ... rnumber=4522548
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2009-01
Authors: Zhou, Deyu
He, Yulan (ORCID Profile 0000-0003-3948-5845)



Version: Accepted Version

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