[Corpora-List] New book: Li: Learning to Rank for IR and NLP

[Graeme Hirst]

NEW BOOK

Learning to Rank for Information Retrieval and Natural Language Processing
Hang Li
April 2011
PDF (3563 KB) | PDF Plus (1635 KB) 

Abstract
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.

Table of Contents: Introduction / Learning for Ranking Creation / Learning for Ranking Aggregation / Methods of Learning to Rank / Applications of Learning to Rank / Theory of Learning to Rank / Ongoing and Future Work

This title is available online free of charge to members of institutions that that have licensed through the Synthesis Digital Library of Engineering and Computer Science. Use of this book as a course text is encouraged; and the text may be downloaded without restriction at licensing institutions, or after a one-time fee of $30 USD at non-licensing schools. To find out whether your institution is a subscriber, visit <http://www.morganclaypool.com/page/licensed>, or follow the links above and attempt to download the PDF. Additional information about Synthesis can be found through the following links, or by contacting me directly. 
Available titles and subject areas: http://www.morganclaypool.com/page/ForthcomingSynthesisLectures
Information for librarians, including pricing and license: http://www.morganclaypool.com/page/librarian_info
A review of Synthesis in ISTL: http://www.istl.org/09-winter/electronic.html

This book can also be purchased in print directly from the Morgan & Claypool Bookstore for $35.00 USD, or from Amazon and other booksellers worldwide.
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