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Title: A latent variable model for query expansion using the hidden Markov model.
Authors: Huang, Qiang
Song, Dawei
Keywords: Hidden Markov model
Information retrieval
Latent variable model
Issue Date: Oct-2008
Publisher: ACM
Citation: 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
Abstract: 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 are viewed as the latent variables and the segmented chunks from the feedback documents are used as the observations given these latent variables. Our extensive experiments shows that our method significantly outperforms a number of strong base- lines in terms of both effectiveness and robustness.
Appears in Collections:Poster / Presentation (Computing)

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