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Statistical Machine Translation

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About the Authors
  • This introductory text to statistical machine translation (SMT) provides all of the theories and methods needed to build a statistical machine translator, such as Google Language Tools and Babelfish. In general, statistical techniques allow automatic translation systems to be built quickly for any language-pair using only translated texts and generic software. With increasing globalization, statistical machine translation will be central to communication and commerce. Based on courses and tutorials, and classroom-tested globally, it is ideal for instruction or self-study, for advanced undergraduates and graduate students in computer science and/or computational linguistics, and researchers in natural language processing. The companion website provides open-source corpora and tool-kits.

    • The first introductory guide to this burgeoning field - takes readers step by step through theory and methods
    • Class tested by the author at universities and conference tutorials
    • Accompanying website provides additional exercises and links to further resources
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    Reviews & endorsements

    "Philipp Koehn has provided the first comprehensive text for this rapidly growing field of statistical machine translation. This book is an invaluable resource for students, researcher, and software developers, providing a lucid and detailed presentation of all the important ideas needed to understand or create a state-of-the-art statistical machine translation system."
    Robert C. Moore, Microsoft Research

    "This is an excellent introduction for someone interested in statistical translation. It is quite readable..."
    Jeffrey Putnam, Computing Reviews

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    Product details

    • Date Published: June 2010
    • format: Adobe eBook Reader
    • isbn: 9780511687594
    • contains: 24 b/w illus. 70 exercises
    • availability: Adobe Reader ebooks available from eBooks.com
  • Table of Contents

    Preface
    Part I. Foundations:
    1. Introduction
    2. Words, sentences, corpora
    3. Probability theory
    Part II. Core Methods:
    4. Word-based models
    5. Phrase-based models
    6. Decoding
    7. Language models
    8. Evaluation
    Part III. Advanced Topics:
    9. Discriminative training
    10. Integrating linguistic information
    11. Tree-based models
    Bibliography
    Author index
    Index.

  • general resources

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    These resources are provided free of charge by Cambridge University Press with permission of the author of the corresponding work, but are subject to copyright. You are permitted to view, print and download these resources for your own personal use only, provided any copyright lines on the resources are not removed or altered in any way. Any other use, including but not limited to distribution of the resources in modified form, or via electronic or other media, is strictly prohibited unless you have permission from the author of the corresponding work and provided you give appropriate acknowledgement of the source.

    If you are having problems accessing these resources please email cflack@cambridge.org

  • Instructors have used or reviewed this title for the following courses

    • Artificial Intelligence
    • Language Engineering
    • Machine Translation
    • Natural Language Processing
    • Natural Language Processing Techniques
    • Statistical Machine Translation
    • Statistical Natural Language Processing
  • Author

    Philipp Koehn, University of Edinburgh
    Philipp Koehn is a lecturer in the School of Informatics at the University of Edinburgh. He is the scientific co-ordinator of the European EuroMatrix project and also involved in research funded by DARPA in the USA. He has also collaborated with leading companies in the field, such as Systran and Asia Online. He implemented the widely used decoder Pharoah, and is leading the development of the open source machine translation toolkit Moses.

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