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    Time-sensitive language modelling for online term recurrence prediction

    Zhang, Dell and Lu, J. and Mao, R. and Nie, J.-Y. (2009) Time-sensitive language modelling for online term recurrence prediction. In: Azzopardi, L. and Kazai, G. and Robertson, S.E. and Ruger, S.M. and Shokouhi, M. and Song, D. and Yilmaz, E. (eds.) Advances in Information Retrieval Theory. Lecture Notes In Computer Science 5766. Berlin, Germany: Springer, pp. 128-138. ISBN 9783642044168.

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    Abstract

    We address the problem of online term recurrence prediction: for a stream of terms, at each time point predict what term is going to recur next in the stream given the term occurrence history so far. It has many applications, for example, in Web search and social tagging. In this paper, we propose a time-sensitive language modelling approach to this problem that effectively combines term frequency and term recency information, and describe how this approach can be implemented efficiently by an online learning algorithm. Our experiments on a real-world Web query log dataset show significant improvements over standard language modelling.

    Metadata

    Item Type: Book Section
    Additional Information: Second International Conference on the Theory of Information Retrieval, ICTIR 2009 Cambridge, UK, September 10-12, 2009 Proceedings
    School: Birkbeck Schools and Departments > School of Business, Economics & Informatics > Computer Science and Information Systems
    Research Centre: Birkbeck Knowledge Lab
    Depositing User: Administrator
    Date Deposited: 30 May 2013 09:39
    Last Modified: 02 Dec 2016 13:26
    URI: http://eprints.bbk.ac.uk/id/eprint/7085

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