Learning to Rank for Information Retrieval and Natural Language Processing, Second Edition (Record no. 86155)

000 -LEADER
fixed length control field 04661nam a22005175i 4500
001 - CONTROL NUMBER
control field 978-3-031-02155-8
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20240730165209.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 220601s2015 sz | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9783031021558
-- 978-3-031-02155-8
082 04 - CLASSIFICATION NUMBER
Call Number 006.3
100 1# - AUTHOR NAME
Author Li, Hang.
245 10 - TITLE STATEMENT
Title Learning to Rank for Information Retrieval and Natural Language Processing, Second Edition
250 ## - EDITION STATEMENT
Edition statement 2nd ed. 2015.
300 ## - PHYSICAL DESCRIPTION
Number of Pages XIII, 107 p.
490 1# - SERIES STATEMENT
Series statement Synthesis Lectures on Human Language Technologies,
505 0# - FORMATTED CONTENTS NOTE
Remark 2 Learning to Rank -- 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 .
520 ## - SUMMARY, ETC.
Summary, etc Learning to rank refers to machine learning techniques for training a 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 its problems recently, and significant progress has been made. This lecture gives an introduction to the area including the fundamental problems, major 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 based, and Neural Network based approaches. The author also introduces some popular learning to rank methods in details. These include: PRank, OC SVM, McRank, Ranking SVM, IR SVM, GBRank, RankNet, ListNet & ListMLE, AdaRank, SVM MAP, SoftRank, LambdaRank, LambdaMART, 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: Learning to Rank / 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.
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier https://doi.org/10.1007/978-3-031-02155-8
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Koha item type eBooks
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-- Cham :
-- Springer International Publishing :
-- Imprint: Springer,
-- 2015.
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-- computer
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-- rdamedia
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-- online resource
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-- text file
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650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Artificial intelligence.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Natural language processing (Computer science).
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computational linguistics.
650 14 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Artificial Intelligence.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Natural Language Processing (NLP).
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computational Linguistics.
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
-- 1947-4059
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-- ZDB-2-SXSC

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