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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.Read more
- 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
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 ResearchSee more reviews
"This is an excellent introduction for someone interested in statistical translation. It is quite readable..."
Jeffrey Putnam, Computing Reviews
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- 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
Part I. Foundations:
2. Words, sentences, corpora
3. Probability theory
Part II. Core Methods:
4. Word-based models
5. Phrase-based models
7. Language models
Part III. Advanced Topics:
9. Discriminative training
10. Integrating linguistic information
11. Tree-based models
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