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Verlag: Springer, 1999
ISBN 10: 0792356853ISBN 13: 9780792356851
Anbieter: Ammareal, Morangis, Frankreich
Buch
Hardcover. Zustand: Très bon. Ancien livre de bibliothèque. Légères traces d'usure sur la couverture. Edition 1999. Ammareal reverse jusqu'à 15% du prix net de cet article à des organisations caritatives. ENGLISH DESCRIPTION Book Condition: Used, Very good. Former library book. Slight signs of wear on the cover. Edition 1999. Ammareal gives back up to 15% of this item's net price to charity organizations.
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Gebraucht ab EUR 26,25
Mehr entdecken Hardcover
Verlag: Springer Netherlands, 2010
ISBN 10: 9048152097ISBN 13: 9789048152094
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Buch
Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - The last decade has been one of dramatic progress in the field of Natural Language Processing (NLP). This hitherto largely academic discipline has found itself at the center of an information revolution ushered in by the Internet age, as demand for human-computer communication and informa tion access has exploded. Emerging applications in computer-assisted infor mation production and dissemination, automated understanding of news, understanding of spoken language, and processing of foreign languages have given impetus to research that resulted in a new generation of robust tools, systems, and commercial products. Well-positioned government research funding, particularly in the U. S. , has helped to advance the state-of-the art at an unprecedented pace, in no small measure thanks to the rigorous 1 evaluations. This volume focuses on the use of Natural Language Processing in In formation Retrieval (IR), an area of science and technology that deals with cataloging, categorization, classification, and search of large amounts of information, particularly in textual form. An outcome of an information retrieval process is usually a set of documents containing information on a given topic, and may consist of newspaper-like articles, memos, reports of any kind, entire books, as well as annotated image and sound files. Since we assume that the information is primarily encoded as text, IR is also a natural language processing problem: in order to decide if a document is relevant to a given information need, one needs to be able to understand its content.
Verlag: Cambridge University Press, 2011
ISBN 10: 0521896134ISBN 13: 9780521896139
Anbieter: Better World Books, Mishawaka, IN, USA
Buch
Zustand: Good. Used book that is in clean, average condition without any missing pages.
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Mehr entdecken Hardcover
Verlag: CRC Press 2023-11-28, Boca Raton, 2023
ISBN 10: 1032154926ISBN 13: 9781032154923
Anbieter: Blackwell's, London, Vereinigtes Königreich
Buch
hardback. Zustand: New. Language: ENG.
Verlag: Springer Nature Singapore, 2023
ISBN 10: 9811699976ISBN 13: 9789811699979
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Buch
Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book gives a comprehensive view of graph theory in informational retrieval (IR) and natural language processing(NLP). This book provides number of graph techniques for IR and NLP applications with examples. It also provides understanding of graph theory basics, graph algorithms and networks using graph. The book is divided into three parts and contains nine chapters. The first part gives graph theory basics and graph networks, and the second part provides basics of IR with graph-based information retrieval. The third part covers IR and NLP recent and emerging applications with case studies using graph theory. This book is unique in its way as it provides a strong foundation to a beginner in applying mathematical structure graph for IR and NLP applications. All technical details that include tools and technologies used for graph algorithms and implementation in Information Retrieval and Natural Language Processing with its future scope are explained in a clear and organized format.
Verlag: Springer Nature Singapore, 2022
ISBN 10: 9811699941ISBN 13: 9789811699948
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Buch
Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book gives a comprehensive view of graph theory in informational retrieval (IR) and natural language processing(NLP). This book provides number of graph techniques for IR and NLP applications with examples. It also provides understanding of graph theory basics, graph algorithms and networks using graph. The book is divided into three parts and contains nine chapters. The first part gives graph theory basics and graph networks, and the second part provides basics of IR with graph-based information retrieval. The third part covers IR and NLP recent and emerging applications with case studies using graph theory. This book is unique in its way as it provides a strong foundation to a beginner in applying mathematical structure graph for IR and NLP applications. All technical details that include tools and technologies used for graph algorithms and implementation in Information Retrieval and Natural Language Processing with its future scope are explained in a clear and organized format.
Verlag: Springer Netherlands, 2005
ISBN 10: 1402033435ISBN 13: 9781402033438
Anbieter: Better World Books, Mishawaka, IN, USA
Buch
Zustand: Very Good. Used book that is in excellent condition. May show signs of wear or have minor defects.
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Mehr entdecken Hardcover
Verlag: Springer Netherlands, 2014
ISBN 10: 9401781672ISBN 13: 9789401781671
Anbieter: moluna, Greven, Deutschland
Buch
Zustand: New.
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Verlag: Springer International Publishing, 2014
ISBN 10: 3031010272ISBN 13: 9783031010279
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Buch
Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.
Verlag: Morgan & Claypool Publishers, 2011
ISBN 10: 1608457079ISBN 13: 9781608457076
Anbieter: Studibuch, Stuttgart, Deutschland
Buch
paperback. Zustand: Gut. 114 Seiten; 9781608457076.3 Sprache: Deutsch Gewicht in Gramm: 500.