Huang, Qiang and Song, Dawei
(2008).
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| URL: | http://dl.acm.org/citation.cfm?id=1458310 |
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| DOI (Digital Object Identifier) Link: | http://dx.doi.org/doi:10.1145/1458082.1458310 |
| Google Scholar: | Look up in Google Scholar |
Abstract
We propose a novel probabilistic method based on the Hidden 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.
| Item Type: | Conference Item |
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| Copyright Holders: | 2008 The Authors |
| Extra Information: | CIKM '08
Proceedings of the 17th ACM Conference on Information and Knowledge Management ACM, New York, NY, 2008 ISBN: 978-1-59593-991-3 pp.1417-1418 |
| Keywords: | Information retrieval, latent variable model, hidden Markov model |
| Academic Unit/Department: | Knowledge Media Institute Mathematics, Computing and Technology > Computing |
| Related URLs: |
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| Item ID: | 35333 |
| Depositing User: | Dawei Song |
| Date Deposited: | 15 Nov 2012 09:49 |
| Last Modified: | 16 Nov 2012 01:56 |
| URI: | http://oro.open.ac.uk/id/eprint/35333 |
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