A latent variable model for query expansion using the Hidden Markov Model

Huang, Qiang; Song, Dawei

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 baselines 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
• Publication Date: Dec 1, 2008
• Deposit Date: May 20, 2009
• Publicly Available Date: May 20, 2009
• Publisher: Association for Computing Machinery (ACM)
• Peer Reviewed: Peer Reviewed
• 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
• Contract Date: May 20, 2009