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Topic-based mixture language modelling

Gotoh, Y. and Renals, S. (1999) Topic-based mixture language modelling. Natural Language Engineering, 5 (4). pp. 355-375. ISSN 1351-3249


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This paper describes an approach for constructing a mixture of language models based on simple statistical notions of semantics using probabilistic models developed for information retrieval. The approach encapsulates corpus-derived semantic information and is able to model varying styles of text. Using such information, the corpus texts are clustered in an unsupervised manner and a mixture of topic-specific language models is automatically created. The principal contribution of this work is to characterise the document space resulting from information retrieval techniques and to demonstrate the approach for mixture language modelling.

A comparison is made between manual and automatic clustering in order to elucidate how the global content information is expressed in the space. We also compare (in terms of association with manual clustering and language modelling accuracy) alternative term-weighting schemes and the effect of singular value decomposition dimension reduction (latent semantic analysis). Test set perplexity results using the British National Corpus indicate that the approach can improve the potential of statistical language modelling. Using an adaptive procedure, the conventional model may be tuned to track text data with a slight increase in computational cost.

Item Type: Article
Copyright, Publisher and Additional Information: © 1999 Cambridge University Press. Reproduced in accordance with the publisher's self-archiving policy.
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield)
Depositing User: Repository Assistant
Date Deposited: 19 Sep 2006
Last Modified: 14 Jun 2014 05:11
Published Version: http://dx.doi.org/10.1017/S1351324900002278
Status: Published
Publisher: Cambridge University Press
Refereed: Yes
Identification Number: 10.1017/S1351324900002278
URI: http://eprints.whiterose.ac.uk/id/eprint/1578

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