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Learning and optimization of an aspect hidden Markov model for query language model generation

Huang, Qiang; Song, Dawei; Rüger, Stefan and Bruza, Peter (2007). Learning and optimization of an aspect hidden Markov model for query language model generation. In: International Conference on the Theory of Information Retrieval, 18-20 Oct 2007.

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Abstract

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.

Item Type: Conference Item
Copyright Holders: 2007 The Authors
ISSN: 1587-2386
Keywords: aspect model; latent variable model; segmentation; information retrieval
Academic Unit/Department: Knowledge Media Institute
Mathematics, Computing and Technology > Computing & Communications
Related URLs:
Item ID: 29587
Depositing User: Stefan Rüger
Date Deposited: 28 Sep 2011 12:19
Last Modified: 24 Oct 2012 06:30
URI: http://oro.open.ac.uk/id/eprint/29587
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