Statistical machine translation : textbook

The Field Of Machine Translation Has Recently Been Energized By The Emergence Of Statistical Techniques, Which Have Brought The Dream Of Automatic Language Translation Closer To Reality. This Class-tested Textbook, Authored By An Active Researcher In The Field, Provides A Gentle And Accessible Introduction To The Latest Methods And Enables The Reader To Build Machine Translation Systems For Any Language Pair. It Provides The Necessary Grounding In Linguistics And Probabilities, And 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 Reports On The Latest Research And Outstanding Challenges, And Enables Novices As Well As Experienced Researchers To Make Contributions To The Field. It Is Ideal For Students At Undergraduate And Graduate Level, Or For Any Reader Interested In The Latest Developments In Machine Translation.--jacket. 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. By Philipp Koehn. Includes Bibliographical References (p. 371-415) And Index. "The field of machine translation has recently been energized by the emergence of statistical techniques, which have brought the dream of automatic language translation closer to reality. This class-tested textbook, authored by an active researcher in the field, provides a gentle and accessible introduction to the latest methods and enables the reader to build machine translation systems for any language pair." "It provides the necessary grounding in linguistics and probabilities, and 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 reports on the latest research and outstanding challenges, and enables novices as well as experienced researchers to make contributions to the field. It is ideal for students at undergraduate and graduate level, or for any reader interested in the latest developments in machine translation."--Page [4] de la couv 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. 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.