Livre: Statistical Language Models for Information Retrieval

[Thierry Hamon]

Date: Tue, 1 Dec 2009 15:15:24 -0500
From: Graeme Hirst <gh at cs.toronto.edu>
Message-Id: <673F91E7-4341-415A-B586-8760F28212B0 at cs.toronto.edu>
X-url: http://dx.doi.org/10.2200/S00158ED1V01Y200811HLT001
X-url: http://www.morganclaypool.com/page/licensed


BOOK ANNOUNCEMENT

Statistical Language Models for Information Retrieval

ChengXiang Zhai (University of Illinois, Urbana-Champaign)

Synthesis Lectures on Human Language Technologies #1 (Morgan &
Claypool Publishers), 2009, 141 pages

As online information grows dramatically, search engines such as
Google are playing a more and more important role in our lives.
Critical to all search engines is the problem of designing an
effective retrieval model that can rank documents accurately for a
given query. This has been a central research problem in information
retrieval for several decades. In the past ten years, a new generation
of retrieval models, often referred to as statistical language models,
has been successfully applied to solve many different information
retrieval problems. Compared with the traditional models such as the
vector space model, these new models have a more sound statistical
foundation and can leverage statistical estimation to optimize
retrieval parameters. They can also be more easily adapted to model
non-traditional and complex retrieval problems. Empirically, they tend
to achieve comparable or better performance than a traditional model
with less effort on parameter tuning. This book systematically reviews
the large body of literature on applying statistical language models
to information retrieval with an emphasis on the underlying
principles, empirically effective language models, and language models
developed for non-traditional retrieval tasks. All the relevant
literature has been synthesized to make it easy for a reader to digest
the research progress achieved so far and see the frontier of research
in this area. The book also offers practitioners an informative
introduction to a set of practically useful language models that can
effectively solve a variety of retrieval problems. No prior knowledge
about information retrieval is required, but some basic knowledge
about probability and statistics would be useful for fully digesting
all the details.

Table of Contents: Introduction / Overview of Information Retrieval
Models / Simple Query Likelihood Retrieval Model / Complex Query
Likelihood Model / Probabilistic Distance Retrieval Model / Language
Models for Special Retrieval Tasks / Language Models for Latent Topic
Analysis / Conclusions

http://dx.doi.org/10.2200/S00158ED1V01Y200811HLT001

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