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Modeling Reformulation Using Query Distributions

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January 29, 2012, at 11:31 PM by 71.234.176.219 -
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  Abstract: Query reformulation modifies the original query with the
to:
Abstract: Query reformulation modifies the original query with the
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   Bio: Xiaobing Xue is PhD candidate from the Center for Intelligent
to:
Bio: Xiaobing Xue is PhD candidate from the Center for Intelligent
January 29, 2012, at 11:31 PM by 71.234.176.219 -
Added lines 1-27:
  Abstract: Query reformulation modifies the original query with the
aim of better matching the vocabulary of the relevant documents,
and consequently improving ranking effectiveness. Previous models
typically generate words and phrases related to the original query,
but do not consider how these words and phrases would fit together in
actual queries. In this paper, a novel framework is proposed that
models reformulation as a distribution of actual queries, where each
query is a variation of the original query. An implementation of this
framework that only uses publicly available resources is proposed,
which makes fair comparisons with other methods using TREC collections
possible. Specifically, this implementation consists of a query
generation step that analyzes the passages containing query words to
generate reformulated queries and a probability estimation step that
learns a distribution for reformulated queries by optimizing the
retrieval performance. Experiments on TREC collections show that the
proposed model can significantly outperform previous reformulation
models.

  Bio: Xiaobing Xue is PhD candidate from the Center for Intelligent
Information Retrieval (CIIR). He is broadly interested in information
retrieval, natural language processing and large-scale machine
learning as well as their practical applications. His current research
focuses on query reformulation, which modifies the original query with
the aim of better matching the vocabulary of relevant documents and
consequently improving the relevance of search systems. His previous
research includes question and answer retrieval, patent retrieval,
multi-modal retrieval and text categorization.
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