Learning to rank for information retrieval and natural language processing /
| Main Author: | |
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| Format: | eBook |
| Language: | English |
| Published: |
San Rafael, California (1537 Fourth Street, San Rafael, CA 94901 USA) :
Morgan & Claypool,
2015.
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| Edition: | Second edition. |
| Series: | Synthesis lectures on human language technologies ;
# 26. |
| Subjects: | |
| Online Access: | Connect to the full text of this electronic book |
| Abstract: | 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. |
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| Physical Description: | 1 online resource (xi, 107 pages) : illustrations. Also available in print. |
| Format: | Mode of access: World Wide Web. System requirements: Adobe Acrobat Reader. |
| Bibliography: | Includes bibliographical references (pages 93-105). |
| ISBN: | 9781627055857 |
| ISSN: | 1947-4059 ; |
| DOI: | 10.2200/S00607ED2V01Y201410HLT026 |
| Access: | Abstract freely available; full-text restricted to subscribers or individual document purchasers. |