Statistical Machine Translation
Cambridge University Press, 12/17/2009
EAN 9780521874151, ISBN10: 0521874157
Hardcover, 446 pages, 24.6 x 17.8 x 2.5 cm
Language: English
The dream of automatic language translation is now closer thanks to recent advances in the techniques that underpin statistical machine translation. This class-tested textbook from an active researcher in the field, provides a clear and careful introduction to the latest methods and explains how to build machine translation systems for any two languages. It introduces the subject's building blocks from linguistics and probability, then covers the major models for machine translation: word-based, phrase-based, and tree-based, as well as machine translation evaluation, language modeling, discriminative training and advanced methods to integrate linguistic annotation. The book also reports the latest research, presents the major outstanding challenges, and enables novices as well as experienced researchers to make novel contributions to this exciting area. Ideal for students at undergraduate and graduate level, or for anyone interested in the latest developments in machine translation.
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.
'Philipp Koehn has provided the first comprehensive text for the rapidly growing field of statistical machine translation. This book is an invaluable resource for students, researchers, 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, Principal Researcher, Microsoft Research