Statistical Language Models for Information Retrieval
systematically and critically reviews the existing work in applying
statistical language models to information retrieval, summarizes
their contributions, and points out outstanding challenges.
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 non-traditional
retrieval problems. In general, statistical language models provide
a principled way of modeling various kinds of retrieval problems.
Statistical Language Models for Information Retrieval reviews the
development of this language modeling approach. It surveys a wide
range of retrieval models based on language modeling and attempts
to make connections between this new family of models and
traditional retrieval models. It summarizes the progress made so
far in these models and point out remaining challenges to be solved
to further increase their impact. Statistical Language Models for
Information Retrieval is written for readers who already have some
basic knowledge about information retrieval. Some knowledge of
probability and statistics such as the maximum likelihood estimator
is helpful, but not a prerequisite to understanding the high-level
discussion.
General
Imprint: |
Now Publishers Inc
|
Country of origin: |
United States |
Series: |
Foundations and Trends (R) in Information Retrieval |
Release date: |
November 2008 |
First published: |
November 2008 |
Authors: |
ChengXiang Zhai
|
Dimensions: |
234 x 156 x 5mm (L x W x T) |
Format: |
Paperback
|
Pages: |
92 |
ISBN-13: |
978-1-60198-186-8 |
Categories: |
Books >
Computing & IT >
General theory of computing >
General
|
LSN: |
1-60198-186-4 |
Barcode: |
9781601981868 |
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