Coverart for item
The Resource Learning to rank for information retrieval and natural language processing, Hang Li, (electronic resource)

Learning to rank for information retrieval and natural language processing, Hang Li, (electronic resource)

Label
Learning to rank for information retrieval and natural language processing
Title
Learning to rank for information retrieval and natural language processing
Statement of responsibility
Hang Li
Creator
Author
Subject
Language
eng
Summary
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
Member of
Cataloging source
CaBNVSL
http://library.link/vocab/creatorDate
1965-
http://library.link/vocab/creatorName
Li, Hang
Dewey number
519.5
Illustrations
illustrations
Index
no index present
LC call number
QA278.75
LC item number
.L55 2015
Literary form
non fiction
Nature of contents
  • dictionaries
  • abstracts summaries
  • bibliography
http://library.link/vocab/subjectName
  • Ranking and selection (Statistics)
  • Information retrieval
  • Natural language processing (Computer science)
  • Machine learning
Target audience
  • adult
  • specialized
Label
Learning to rank for information retrieval and natural language processing, Hang Li, (electronic resource)
Instantiates
Publication
Note
  • Part of: Synthesis digital library of engineering and computer science
  • Series from website
Bibliography note
Includes bibliographical references (p. 89-100)
Color
multicolored
Contents
  • Preface -- 1. Learning to rank -- Ranking -- Learning to rank -- Ranking creation -- Ranking aggregation -- Learning for ranking creation -- Learning for ranking aggregation --
  • 2. Learning for ranking creation -- Document retrieval as example -- Learning task -- Training and testing -- Training data creation -- Feature construction -- Evaluation -- Relations with other learning tasks -- Learning approaches -- Pointwise approach -- Pairwise approach -- Listwise approach -- Evaluation results --
  • 3. Learning for ranking aggregation -- Learning task -- Learning methods --
  • 4. Methods of learning to rank -- PRank -- Model -- Learning algorithm -- OC SVM -- Model -- Learning algorithm -- Ranking SVM -- Linear model as ranking function -- Ranking SVM model -- Learning algorithm -- IR SVM -- Modified loss function -- Learning algorithm -- GBRank -- Loss function -- Learning algorithm -- RankNet -- Loss function -- Model -- Learning algorithm -- Speed up of training -- LambdaRank -- Loss function -- Learning algorithm -- ListNet and ListMLE -- Plackett-Luce model -- ListNet -- ListMLE -- AdaRank -- Loss function -- Learning algorithm -- SVM map -- Loss function -- Learning algorithms -- SoftRank -- Soft NDCG -- Approximation of rank distribution -- Learning algorithm -- Borda count -- Markov chain -- Cranking -- Model -- Learning algorithm -- Prediction --
  • 5. Applications of learning to rank --
  • 6. Theory of learning to rank -- Statistical learning formulation -- Loss functions -- Relations between loss functions -- Theoretical analysis --
  • 7. Ongoing and future work -- Bibliography -- Author's biography
Control code
6813404
Dimensions
unknown
Extent
1 electronic text (ix, 101 p.)
File format
multiple file formats
Form of item
online
Governing access note
Abstract freely available; full-text restricted to subscribers or individual document purchasers
Isbn
9781608457083
Other control number
10.2200/S00348ED1V01Y201104HLT012
Other physical details
ill., digital file.
Reformatting quality
access
Specific material designation
remote
System details
  • Mode of access: World Wide Web
  • System requirements: Adobe Acrobat Reader
Label
Learning to rank for information retrieval and natural language processing, Hang Li, (electronic resource)
Publication
Note
  • Part of: Synthesis digital library of engineering and computer science
  • Series from website
Bibliography note
Includes bibliographical references (p. 89-100)
Color
multicolored
Contents
  • Preface -- 1. Learning to rank -- Ranking -- Learning to rank -- Ranking creation -- Ranking aggregation -- Learning for ranking creation -- Learning for ranking aggregation --
  • 2. Learning for ranking creation -- Document retrieval as example -- Learning task -- Training and testing -- Training data creation -- Feature construction -- Evaluation -- Relations with other learning tasks -- Learning approaches -- Pointwise approach -- Pairwise approach -- Listwise approach -- Evaluation results --
  • 3. Learning for ranking aggregation -- Learning task -- Learning methods --
  • 4. Methods of learning to rank -- PRank -- Model -- Learning algorithm -- OC SVM -- Model -- Learning algorithm -- Ranking SVM -- Linear model as ranking function -- Ranking SVM model -- Learning algorithm -- IR SVM -- Modified loss function -- Learning algorithm -- GBRank -- Loss function -- Learning algorithm -- RankNet -- Loss function -- Model -- Learning algorithm -- Speed up of training -- LambdaRank -- Loss function -- Learning algorithm -- ListNet and ListMLE -- Plackett-Luce model -- ListNet -- ListMLE -- AdaRank -- Loss function -- Learning algorithm -- SVM map -- Loss function -- Learning algorithms -- SoftRank -- Soft NDCG -- Approximation of rank distribution -- Learning algorithm -- Borda count -- Markov chain -- Cranking -- Model -- Learning algorithm -- Prediction --
  • 5. Applications of learning to rank --
  • 6. Theory of learning to rank -- Statistical learning formulation -- Loss functions -- Relations between loss functions -- Theoretical analysis --
  • 7. Ongoing and future work -- Bibliography -- Author's biography
Control code
6813404
Dimensions
unknown
Extent
1 electronic text (ix, 101 p.)
File format
multiple file formats
Form of item
online
Governing access note
Abstract freely available; full-text restricted to subscribers or individual document purchasers
Isbn
9781608457083
Other control number
10.2200/S00348ED1V01Y201104HLT012
Other physical details
ill., digital file.
Reformatting quality
access
Specific material designation
remote
System details
  • Mode of access: World Wide Web
  • System requirements: Adobe Acrobat Reader

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