Discriminative Methods for Improving Named Entity Extraction on Speech Data

James Horlock, Simon King

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In this paper we present a method of discriminatively training language models for spoken language understanding; we show improvements in named entity F-scores on speech data using these improved language models. A comparison between theoretical probabilities associated with manual markup and the actual probabilities of output markup is used to identify probabilities requiring adjustment. We present results which support our hypothesis that improvements in F-scores are possible by using either previously used training data or held out development data to improve discrimination amongst a set of N-gram language models.
Original languageEnglish
Title of host publicationEurospeech 2003 - Interspeech 2003
Subtitle of host publication8th European Conference on Speech Communication and Technology
PublisherInternational Speech Communication Association
Number of pages4
ISBN (Print)ISSN: 1990-9772
Publication statusPublished - 1 Sep 2003

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