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Author:   Marie-Francine Moens
Publisher:   Springer
Imprint:   Springer
Edition:   Softcover reprint of hardcover 1st ed. 2006
Volume:   21
Dimensions:   Width: 16.00cm , Height: 1.50cm , Length: 24.00cm
Weight:   0.454kg
ISBN:  

9789048172467

ISBN 10:   9048172462
Pages:   246
Publication Date:   22 November 2010
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Awaiting stock  
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Table of Contents

Information Extraction and Information Technology.- Information Extraction from an Historical Perspective.- The Symbolic Techniques.- Pattern Recognition.- Supervised Classification.- Unsupervised Classification Aids.- Integration of Information Extraction in Retrieval Models.- Evaluation of Information Extraction Technologies.- Case Studies.- The Future of Information Extraction in a Retrieval Context.

Reviews

From the reviews: Information Extraction (IE) and Information Retrieval (IR) are core enabling technologies ! . In this text, Moens brings these two techniques together to illustrate how information derived using IE could be highly beneficial in IR systems. ! the text is highly readable and aimed at both practitioners and researchers ! . One trait that I offer particular praise to the author for is the pragmatic presentation of ideas. ! the text should be beneficial both to seasoned professionals in this area and relative newcomers. (Tom Betts, Informer, Winter 2006/2007) After definition and explanation of the basic concepts and description of the historical development of the area, the past and current most successful algorithms and their application in a variety of domains are discussed. Especially important is the explanation of statistical and machine learning algorithms for information detection and classification and integration of their results in probabilistic retrieval models. ! Because its broad coverage and clear and sound explanation it is suitable and valuable both for researchers and for students. (Antonin Riha, Zentralblatt MATH, Vol. 1108 (10), 2007) This book ! provide a comprehensive overview of text-extraction algorithms. It does well in ! explaining the intricacies of the basic approaches and concepts used. ! for advanced undergraduate students, graduate students, researchers, and people working in the field, the book is a good starting point for learning the basics. ! I would recommend the book for those who need to get into ! the field. ! the book is one that should be on your must-read list if you are involved in this field. (Karthik Gajjala, ACM Computing Reviews, Vol. 49 (2), February, 2008)

From the reviews: Information Extraction (IE) and Information Retrieval (IR) are core enabling technologies ... . In this text, Moens brings these two techniques together to illustrate how information derived using IE could be highly beneficial in IR systems. ... the text is highly readable and aimed at both practitioners and researchers ... . One trait that I offer particular praise to the author for is the pragmatic presentation of ideas. ... the text should be beneficial both to seasoned professionals in this area and relative newcomers. (Tom Betts, Informer, Winter 2006/2007) After definition and explanation of the basic concepts and description of the historical development of the area, the past and current most successful algorithms and their application in a variety of domains are discussed. Especially important is the explanation of statistical and machine learning algorithms for information detection and classification and integration of their results in probabilistic retrieval models. ... Because its broad coverage and clear and sound explanation it is suitable and valuable both for researchers and for students. (Antonin Riha, Zentralblatt MATH, Vol. 1108 (10), 2007) This book ... provide a comprehensive overview of text-extraction algorithms. It does well in ... explaining the intricacies of the basic approaches and concepts used. ... for advanced undergraduate students, graduate students, researchers, and people working in the field, the book is a good starting point for learning the basics. ... I would recommend the book for those who need to get into ... the field. ... the book is one that should be on your must-read list if you are involved in this field. (Karthik Gajjala, ACM Computing Reviews, Vol. 49 (2), February, 2008)

From the reviews: Information Extraction (IE) and Information Retrieval (IR) are core enabling technologies ... . In this text, Moens brings these two techniques together to illustrate how information derived using IE could be highly beneficial in IR systems. ... the text is highly readable and aimed at both practitioners and researchers ... . One trait that I offer particular praise to the author for is the pragmatic presentation of ideas. ... the text should be beneficial both to seasoned professionals in this area and relative newcomers. (Tom Betts, Informer, Winter 2006/2007) After definition and explanation of the basic concepts and description of the historical development of the area, the past and current most successful algorithms and their application in a variety of domains are discussed. Especially important is the explanation of statistical and machine learning algorithms for information detection and classification and integration of their results in probabilistic retrieval models. ... Because its broad coverage and clear and sound explanation it is suitable and valuable both for researchers and for students. (Antonin Riha, Zentralblatt MATH, Vol. 1108 (10), 2007) This book ... provide a comprehensive overview of text-extraction algorithms. It does well in ... explaining the intricacies of the basic approaches and concepts used. ... for advanced undergraduate students, graduate students, researchers, and people working in the field, the book is a good starting point for learning the basics. ... I would recommend the book for those who need to get into ... the field. ... the book is one that should be on your must-read list if you are involved in this field. (Karthik Gajjala, ACM Computing Reviews, Vol. 49 (2), February, 2008)

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