Now showing items 1-4 of 4

  • Facilitating query decomposition in query language modeling by association rule mining using multiple sliding windows. 

    Song, Dawei; Huang, Qiang; Ruger, Stefan; Bruza, Peter D. (Springer http://dx.doi.org/10.1007/978-3-540-786-7_31, 2008)
    SONG, D., HUANG, Q., RUGER, S. and BRUZA, P. D., 2008. Facilitating query decomposition in query language modeling by association rule mining using multiple sliding windows. In: C. MACDONALD, I. OUNIS, V. PLACHOURAS, I. RUTHVEN and R.W. WHITE, eds. Advances in Information Retrieval: 30th European Conference on IR Research, ECIR 2008, Glasgow, UK, March 30-April 3, 2008; Proceedings. Berlin: Springer. pp. 334-345.
    This paper presents a novel framework to further advance the recent trend of using query decomposition and high-order term re- lationships in query language modeling, which takes into account terms implicitly associated ...
  • A latent variable model for query expansion using the hidden Markov model. 

    Huang, Qiang; Song, Dawei (ACM http://doi.acm.org/10.1145/1458082.1458310, 2008-10)
    HUANG, Q. and SONG, D. 2008. A latent variable model for query expansion using the hidden Markov model. In: J. G. SHANAHAN, S. AMER-YAHIA, I. MANOLESCU, Y. ZHANG, D. A. EVANS, A. KOLCZ, K.-S. CHOI and A. CHOWDURY, eds. Proceedings of the ACM 17th Conference on Information and Knowledge Management. 26-30 October 2008. New York: ACM. pp. 1417-1418
    We propose a novel probabilistic method based on the Hid- den Markov Model (HMM) to learn the structure of a Latent Variable Model (LVM) for query language modeling. In the proposed LVM, the combinations of query terms ...
  • Learning and optimization of an aspect hidden Markov model for query language model generation. 

    Huang, Qiang; Song, Dawei; Roger, Stefan; Bruza, Peter D. (Infota (Foundation for Information Society, Budapest), 2007-10)
    HUANG, Q., SONG, D., RUGER, S. and BRUZA, P. D., 2007. Learning and optimization of an aspect hidden Markov model for query language model generation. In: S. DOMINICH and F. KISS, eds. Proceedings of the 1st International Conference on the Theory of Information Retrieval (ICTIR 2007). 18-20 October 2007. Budapest, Hungary: Infota. Pp. 157-164.
    The Relevance Model (RM) incorporates pseudo relevance feedback to derive query language model and has shown a good performance. Generally, it is based on uni-gram models of individual feedback documents from which query ...
  • Robust query-specific pseudo feedback document selection for query expansion. 

    Huang, Qiang; Song, Dawei; Ruger, Stefan (Springer http://dx.doi.org/10.1007/978-3-540-78646-7_54, 2008)
    HUANG, Q., SONG, D. and RUGER, S., 2008. Robust query-specific pseudo feedback document selection for query expansion. In: C. MACDONALD, I. OUNIS, V. PLACHOURAS, I. RUTHVEN and R.W. WHITE, eds. Advances in Information Retrieval: 30th European Conference on IR Research, ECIR 2008, Glasgow, UK, March 30-April 3, 2008; Proceedings. Berlin: Springer. pp. 547- 554
    In document retrieval using pseudo relevance feedback, after initial ranking, a fixed number of top-ranked documents are selected as feedback to build a new expansion query model. However, very little at- tention has ...