文档介绍：Statistical Language Models
for Information Retrieval
Synthesis Lectures on
Human Language
Technologies
Editor
Graeme Hirst
University of Toronto
Synthesis Lectures on Human LanguageTechnologies publishes monographs on topics relating to natural
language putational linguistics,information retrieval,and spoken language understanding.
Emphasis is placed on important new techniques, on new applications, and on topics bine two
or more HLT subfields.
Statistical Language Models for Information Retrieval
ChengXiang Zhai
2009
Copyright © 2009 by Morgan & Claypool
All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in
any form or by any means—electronic, mechanical, photocopy, recording, or any other except for brief quotations in
printed reviews, without the prior permission of the publisher.
Statistical Language Models for Information Retrieval
ChengXiang Zhai
ISBN: 9781598295900 paperback
ISBN: 9781598295917 ebook
DOI
A Publication in the Morgan & Claypool Publishers series
SYNTHESIS LECTURES ON HUMAN LANGUAGE TECHNOLOGIES
Lecture #1
Series Editor: Graeme Hirst, University of Toronto
Series ISSN
Synthesis Lectures on Human Language Technologies
ISSN pending.
Statistical Language Models
for Information Retrieval
ChengXiang Zhai
Department puter Science
Graduate School of Library and Information Science
Department of Statistics
Institute for Genomic Biology
University of Illinois at Urbana-Champaign
SYNTHESIS LECTURES ON HUMAN LANGUAGE TECHNOLOGIES #1
M
&C Morgan& cLaypool publishers
ABSTRACT
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 has been a central research
problem in information retr