Learning and Optimization of an Aspect Hidden Markov Model for Query Language Model Generation

Huang, Qiang, Song, Dawei, Ruger, Stefan, & Bruza, Peter D. (2007) Learning and Optimization of an Aspect Hidden Markov Model for Query Language Model Generation. In Dominich, S. & Kiss, F. (Eds.) 1st International Conference on the Theory of Information Retrieval: Studies in Theory of Information Retrieval, 18 - 20 October 2007, Budapest, Hungary.

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The Relevance Model (RM) incorporates pseudo relevance feedback to derive query language model and has shown a good performance. Generally, it is based on uni-gram models of individual feedback documents from which query terms are sampled independently. In this paper, we present a new method to build the query model with latent state machine (LSM) which captures the inherent term dependencies within the query and the term dependencies between query and documents. Our method firstly splits the query into subsets of query terms (i.e., not only single terms, but different combinations of multiple query terms). Secondly, these query term combinations are then considered as weighted latent states of a hidden Markov Model to derive a new query model from the pseudo relevant documents. Thirdly, our method integrates the Aspect Model (AM) with the EM algorithm to estimate the parameters involved in the model. Specifically, the pseudo relevant documents are segmented into chunks, and different chunks are associated with different weights in relation to a latent state. Our approach is empirically evaluated on three TREC collections, and demonstrates statistically significant improvements over a baseline language model and the Relevance Model.

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ID Code: 15711
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
Keywords: Aspect Model, Latent Variable Model, Segmentation, Information Retrieval
ISBN: 9789630632379
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > LIBRARY AND INFORMATION STUDIES (080700) > Information Retrieval and Web Search (080704)
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Copyright Owner: Copyright 2007 (please consult author)
Deposited On: 24 Nov 2008 00:00
Last Modified: 29 Feb 2012 13:36

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