Statistical machine translation / Philipp Koehn.

Title from publisher's bibliographic system (viewed on 05 Oct 2015).

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.

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.