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Morgan Claypool Publishers
&
SYNTHESIS LECTURES ON
HUMAN LANGUAGE TECHNOLOGIES
www.morganclaypool.com
Series Editor: Graeme Hirst, University of Toronto
MORGAN&CLAYPOOL
C
M
&
Morgan Claypool Publishers
&
About SYNTHESIs
This volume is a printed version of a work that appears in the Synthesis
Digital Library of Engineering and Computer Science. Synthesis Lectures
provide concise, original presentations of important research and development
topics, published quickly, in digital and print formats. For more information
visit www.morganclaypool.com
SYNTHESIS LECTURES ON
HUMAN LANGUAGE TECHNOLOGIES
LEARNING TO RANK FOR INFORMATION RETRIEVAL AND NATURAL LANGUAGE PROCESSING
Learning to Rank for
Information Retrieval
and Natural Language
Processing
Graeme Hirst, Series Editor
ISBN: 978-1-60845-707-6
9 781608 457076
90000
Series ISSN: 1947-4040
Learning to Rank for Information Retrieval and Natural Language Processing
Hang Li, Microsoft
Learning to rank refers to machine learning techniques for training the model in a ranking task. Learning
to rank is useful for many applications in information retrieval, natural language processing, and data mining.
Intensive studies have been conducted on the problem recently and significant progress has been made. This
lecture gives an introduction to the area including the fundamental problems, existing approaches, theories,
applications, and future work.
The author begins by showing that various ranking problems in information retrieval and natural language
processing can be formalized as two basic ranking tasks, namely ranking creation (or simply ranking) and
ranking aggregation. In ranking creation, given a request, one wants to generate a ranking list of offerings
based on the features derived from the request and the offerings. In ranking aggregation, given a request, as
well as a number of ranking lists of offerings, one wants to generate a new ranking list of the offerings.
Ranking creation (or ranking) is the major problem in learning to rank. It is usually formalized as a
supervised learning task. The author gives detailed explanations on learning for ranking creation and ranking
aggregation, including training and testing, evaluation, feature creation, and major approaches. Many methods
have been proposed for ranking creation. The methods can be categorized as the pointwise, pairwise, and
listwise approaches according to the loss functions they employ. They can also be categorized according to
the techniques they employ, such as the SVM based, Boosting SVM, Neural Network based approaches.
The author also introduces some popular learning to rank methods in details. These include PRank, OC
SVM, Ranking SVM, IR SVM, GBRank, RankNet, LambdaRank, ListNet & ListMLE, AdaRank, SVM
MAP, SoftRank, Borda Count, Markov Chain, and CRanking.
The author explains several example applications of learning to rank including web search, collaborative
filtering, definition search, keyphrase extraction, query dependent summarization, and re-ranking in machine
translation.
A formulation of learning for ranking creation is given in the statistical learning framework. Ongoing
and future research directions for learning to rank are also discussed.
Hang Li
LI
Learning to Rank for
Information Retrieval
and Natural Language Processing
Synthesis Lectures on Human
Language Technology
Editor
Graeme Hirst, University of Toronto
Synthesis Lectures on Human Language Technologies is edited by Graeme Hirst of the University of
Toronto. The series consists of 50- to 150-page monographs on topics relating to natural language
processing, computational linguistics, information retrieval, and spoken language understanding.
Emphasis is on important new techniques, on new applications, and on topics that combine two or
more HLT subfields.
Learning to Rank for Information Retrieval and Natural Language Processing
Hang Li
2011
Computational Modeling of Human Language Acquisition
Afra Alishahi
2010
Introduction to Arabic Natural Language Processing
Nizar Y. Habash
2010
Cross-Language Information Retrieval
Jian-Yun Nie
2010
Automated Grammatical Error Detection for Language Learners
Claudia Leacock, Martin Chodorow, Michael Gamon, and Joel Tetreault
2010
Data-Intensive Text Processing with MapReduce
Jimmy Lin and Chris Dyer
2010
Semantic Role Labeling
Martha Palmer, Daniel Gildea, and Nianwen Xue
2010
iii
Spoken Dialogue Systems
Kristiina Jokinen and Michael McTear
2009
Introduction to Chinese Natural Language Processing
Kam-Fai Wong, Wenjie Li, Ruifeng Xu, and Zheng-sheng Zhang
2009
Introduction to Linguistic Annotation and Text Analytics
Graham Wilcock
2009
Dependency Parsing
Sandra Kübler, Ryan McDonald, and Joakim Nivre
2009
Statistical Language Models for Information Retrieval
ChengXiang Zhai
2008
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