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Viewing report
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Content-Based Mining of Query Replacements. Edition No. 1
VDM Publishing House, Oct 2008, Pages: 76
This dissertation introduces the problem of content-
based query replacement. It motivates
the term correlation modeled using MRFs and the
sampling based technique to learn the
search phrase definitions. These definitions are
used as alternative queries to achieve higher
accuracy in several retrieval tasks.
Retrieving relevant documents while eliminating
irrelevant documents for an user’s
query is a challenging task which involves a good
understanding of the relation between
the data and the query as well as developing
algorithms that can efficiently measure the
relevance of the data to the query. As part of this
dissertation, we have developed a hypothesis to
reduce the problem of mining search phrase
definitions significantly
by modeling the joint distribution of terms as a
product of conditional distributions,
modeled as a Markov Random Field. We assume that
there exists an underlying joint
distribution among terms that are used to describe
the search phrase. The modeling
we propose is a condensed representation of inter-
term relationship and it appears to
capture insight statistics among terms.
describes a target phrase.
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