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A latent variable model for query expansion using the hidden Markov model.

Huang, Qiang; Song, Dawei

Authors

Qiang Huang

Dawei Song



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.

Citation

HUANG, Q. and SONG, D. 2008. A latent variable model for query expansion using the hidden Markov model. In Proceedings of the 17th Association for Computing Machinery (ACM) international conference on information and knowledge management (CIKM'08), 26-30 October 2008, Napa Valley, USA. New York: ACM [online], pages 1417-1418. Available from: https://doi.org/10.1145/1458082.1458310

Presentation Conference Type Poster
Conference Name 17th Association for Computing Machinery (ACM) international conference on information and knowledge management (CIKM'08)
Start Date Oct 26, 2008
End Date Oct 30, 2008
Deposit Date May 20, 2009
Publicly Available Date May 20, 2009
Publisher Association for Computing Machinery
Pages 1417-1418
DOI https://doi.org/10.1145/1458082.1458310
Keywords Hidden Markov model; Information retrieval; Latent variable model
Public URL http://hdl.handle.net/10059/340

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