Recent Submissions

  • A comparison of various approaches for using probabilistic dependencies in language modeling. 

    Bruza, Peter D.; Song, Dawei (ACM http://doi.acm.org/10.1145/860435.860530, 2003-08)
    BRUZA, P. D. and SONG, D., 2003. A comparison of various approaches for using probabilistic dependencies in language modeling. In: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR2003). 28 July – 01 August 2003. New York: ACM. pp. 419-420
    The goals of this article is to study several estimates of relevance models which will be computed based on differing approaches for incorporating term dependency information. In this way, we hope to shed light on the ...
  • 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 ...
  • Dimensionality reduction for dimension-specific search. 

    Huang, Zi; Shen, Heng Tao; Zhou, Xiaofang; Song, Dawei; Ruger, Stefan (ACM http://doi.acm.org/10.1145/1277741.1277940, 2007)
    HUANG, Z., SHEN, H., ZHOU, X., SONG, D. and RUGER, S. 2007. Dimensionality reduction for dimension-specific search. In: C. CLARKE, N. FUHR, N. KANDO, W. KRAAIJ and A. DE VRIES, eds. Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 23-27 July 2007. Amsterdam. pp. 849-850
    Dimensionality reduction plays an important role in efficient similarity search, which is often based on k-nearest neighbor (k-NN) queries over a high-dimensional feature space. In this paper, we introduce a novel type ...

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