Statistical Machine Translation

封面
Cambridge University Press, 2010 - 433 頁
La 4e de couverture indique : "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."
 

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內容

Words Sentences Corpora
33
Probability Theory
63
WordBased Models
81
PhraseBased Models
127
Decoding
155
Language Models
181
Evaluation
217
Discriminative Training
249
Integrating Linguistic Information
289
TreeBased Models
331
Bibliography
371
Author Index
416
Index
427
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關於作者 (2010)

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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