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Statistical Language Models for Information Retrieval: <br>A Critical Review
Foundations and Trends® in
Information Retrieval
Volume 2 Issue 3
DOI: 10.1561/1500000008
Statistical Language Models for Information Retrieval:
A Critical Review
ChengXiang Zhai
University of Illinois at Urbana-Champaign, 201 N. Goodwin, Urbana, IL 61801, USA, czhai@cs.uiuc.edu
SUGGESTED CITATION:
ChengXiang
Zhai
(2008)
"Statistical Language Models for Information Retrieval A Critical Review",
Foundations and Trends® in Information Retrieval: Vol. 2: No 3, pp 137-213.
http:/dx.doi.org/10.1561/1500000008
Abstract
Statistical language models have recently been successfully applied to many information retrieval problems. A great deal of
recent work has shown that statistical language models not only lead to superior empirical performance, but also facilitate
parameter tuning and open up possibilities for modeling nontraditional retrieval problems. In general, statistical language
models provide a principled way of modeling various kinds of retrieval problems. The purpose of this survey is to systematically
and critically review the existing work in applying statistical language models to information retrieval, summarize their
contributions, and point out outstanding challenges.
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