Learning to rank for information retrieval and natural language processing
By: Hang Li
Series: Synthesis lectures on human language technology -- # 12
Publisher: Morgan & Claypool
Published: 2011
Place of publication: San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA)
Language: eng
Format: [electronic resource] /
Number of pages: 114
ISBN-13: 9781608457083; 9781608457076
Subjects: Ranking and selection (Statistics); Information retrieval; Natural language processing (Computer science); Machine learning
Other titles: Synthesis digital library of engineering and computer science.
Description: 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.
Notes: Part of: Synthesis digital library of engineering and computer science.

Series from website.

Includes bibliographical references (p. 89-100).

Abstract freely available; full-text restricted to subscribers or individual document purchasers.

Also available in print.

Mode of access: World Wide Web.

System requirements: Adobe Acrobat Reader.
Table of contents: title: Preface; title: 1. Learning to rank; title: Ranking; title: Learning to rank; title: Ranking creation; title: Ranking aggregation; title: Learning for ranking creation; title: Learning for ranking aggregation; title: 2. Learning for ranking creation; title: Document retrieval as example; title: Learning task; title: Training and testing; title: Training data creation; title: Feature construction; title: Evaluation; title: Relations with other learning tasks; title: Learning approaches; title: Pointwise approach; title: Pairwise approach; title: Listwise approach; title: Evaluation results; title: 3. Learning for ranking aggregation; title: Learning task; title: Learning methods; title: 4. Methods of learning to rank; title: PRank; title: Model; title: Learning algorithm; title: OC SVM; title: Model; title: Learning algorithm; title: Ranking SVM; title: Linear model as ranking function; title: Ranking SVM model; title: Learning algorithm; title: IR SVM; title: Modified loss function; title: Learning algorithm; title: GBRank; title: Loss function; title: Learning algorithm; title: RankNet; title: Loss function; title: Model; title: Learning algorithm; title: Speed up of training; title: LambdaRank; title: Loss function; title: Learning algorithm; title: ListNet and ListMLE; title: Plackett-Luce model; title: ListNet; title: ListMLE; title: AdaRank; title: Loss function; title: Learning algorithm; title: SVM map; title: Loss function; title: Learning algorithms; title: SoftRank; title: Soft NDCG; title: Approximation of rank distribution; title: Learning algorithm; title: Borda count; title: Markov chain; title: Cranking; title: Model; title: Learning algorithm; title: Prediction; title: 5. Applications of learning to rank; title: 6. Theory of learning to rank; title: Statistical learning formulation; title: Loss functions; title: Relations between loss functions; title: Theoretical analysis; title: 7. Ongoing and future work; title: Bibliography; title: Author's biography.