Download PDF Handbook of Natural Language Processing (Chapman & Hall/CRC: Machine Learning & Pattern Recognition)
Download PDF Handbook of Natural Language Processing (Chapman & Hall/CRC: Machine Learning & Pattern Recognition)
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Handbook of Natural Language Processing (Chapman & Hall/CRC: Machine Learning & Pattern Recognition)
Download PDF Handbook of Natural Language Processing (Chapman & Hall/CRC: Machine Learning & Pattern Recognition)
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Review
… The need for a revised second edition of this book arose because of the growth of the field and the introduction of new methods. … The chapters have been exhaustively reviewed to maintain quality and homogeneity. The handbook has numerous diagrams and tables. The chapters are arranged so that they may be read independently. The style of presentation is good and the index is useful. Adequate references to current literature are provided. When compared to the previous edition, this edition focuses on statistical approaches, new and emerging applications, and multilingual scope, and has an actively maintained Wiki. Outdated chapters present in the first edition have been removed, and the remaining chapters have been rewritten and updated to reflect current trends and applications. When compared to other handbooks on NLP, this one is cheaper and certainly worth every penny. It provides a lot of useful information to those who are interested in NLP and its applications. … I highly recommend this handbook to practitioners of NLP as a very useful resource.―Computing Reviews, January 2011 … the handbook covers the wide area of NLP and its applications. This will essentially help researchers and graduate students to access starting-point material for a particular area of interest. The handbook also covers the associated algorithms with examples which will help to develop prototype systems … a high quality compilation of up-to-date theories and applications of NLP.― Sandipan Dandapat … If you need a readable introduction to this important subject ― this is it. … This is a good way to get into NLP. … this does provide a basic course on the subject suitable both for academic and practical development. Highly recommended.―Mike James, iProgrammer, 2010
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About the Author
Nitin Indurkhya is an associate professor in the School of Computer Science and Engineering at the University of New South Wales in Sydney, Australia. He is also the founder and president of Data-Miner Pty Ltd, which offers education, training, and consulting services in data/text analytics and human language technologies. Before his death, Fred J. Damerau was a researcher at IBM’s Thomas J. Watson Research Center in Yorktown Heights, New York, where he worked on machine learning approaches to natural language processing.
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Product details
Series: Chapman & Hall/CRC: Machine Learning & Pattern Recognition
Hardcover: 702 pages
Publisher: Chapman and Hall/CRC; 2 edition (February 22, 2010)
Language: English
ISBN-10: 9781420085921
ISBN-13: 978-1420085921
ASIN: 1420085921
Product Dimensions:
7 x 1.5 x 10 inches
Shipping Weight: 3.1 pounds (View shipping rates and policies)
Average Customer Review:
4.0 out of 5 stars
3 customer reviews
Amazon Best Sellers Rank:
#2,136,969 in Books (See Top 100 in Books)
This is the second edition of Nitkin Indurkhya and Fred Damerau's guide to natural language processing (NLP). Damerau passed away before this edition was published, but his contributions are present and acknowledged throughout. Like the first edition, this volume has a practical focus and is targeted at language-engineering professionals. Its stated goals are to focus on practical tools and techniques and discuss NLP as it pertains to input to and output from computer systems. The second edition includes greater coverage of NLP in non-English languages and has a companion wiki with post-publication content and links to useful online resources.The handbook is organized into three sections. The first, Classical Approaches, covers historical and foundational roots of the field. Its chapters introduce techniques for organizing text data, parsing it into words and other meaningful units, and conducting basic syntactic and semantic analyses. A final chapter introduces language generation. The second section presents modern empirical/statistical NLP. It divides the territory as linguists would expect. Separate chapters cover creation and management of large samples of language, statistical techniques, parsing and part-of-speech tagging, word sense disambiguation, and speech recognition and translation. The third selection examines some representative NLP applications, including machine translation, question answering, and text mining.The book presents a great deal of densely-technical information in a fairly readable manner. (The statistics chapter is an exception; it could use additional detail and a less theory-driven emphasis.) It is not intended as a textbook, so the reader shouldn't expect much hand-holding. Nor is the coverage of topics comprehensive. But there are numerous useful references in the text and links in the book's wiki to more detailed sources. Although there are no exercises per se, the example procedures are presented well. It is a useful handbook and reference that is comparable to--and updates--Manning and Schuetze's Foundations of Statistical Natural Language Processing.So check it out of the library and give it a skim. If you are working in this area, consider obtaining your own mark-up-able desk copy.
Good reference for my research
Extremely complete, very difficult to read.
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