A latent variable model for query expansion using the hidden markov model

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. Categories and Subject Descriptors H.3 [Information Storage and Retrieval]: Retrieval Models General Terms Algorithm Keywords Information retrieval, latent variable model, hidden Markov model

• Type: Conference
• Year: 2008
• Where: CIKM
• Authors: Qiang Huang, Dawei Song
• Keywords: CIKM 2008, Hidden Markov Model, Information Management, Latent Variable, Latent Variable Model