- Gebundene Ausgabe: 446 Seiten
- Verlag: Cambridge University Press; Auflage: 1 (17. Dezember 2009)
- Sprache: Englisch
- ISBN-10: 0521874157
- ISBN-13: 978-0521874151
- Größe und/oder Gewicht: 17,4 x 2,5 x 24,7 cm
- Durchschnittliche Kundenbewertung: 2 Kundenrezensionen
- Amazon Bestseller-Rang: Nr. 224.938 in Fremdsprachige Bücher (Siehe Top 100 in Fremdsprachige Bücher)
- Komplettes Inhaltsverzeichnis ansehen
Statistical Machine Translation (Englisch) Gebundene Ausgabe – 17. Dezember 2009
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'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
'The book primarily represents an ideal introduction to the field of statistical machine translation, but also tackles many of the recent results in this area. It is the product of the many years of both active research and extensive teaching of the author … Each chapter is additionally endowed with a summary, further reading and exercises, achieving thus completely the proposed goal of an accessible introduction to the statistical machine translation field. Apart from its formative role for beginners, the book also stands as a complete guide for researchers in a domain of high interest and rapid expansion … For all these reasons, this book should be welcomed as a highly valuable publication.' Zentralblatt MATH
'… Statistical Machine Translation provides an excellent synthesis of a vast amount of literature (the bibliography section takes up 45 double-column pages) and presents it in a well-structured and articulate way. Moreover, the book has been class-tested and contains a set of exercises at the end of each chapter, as well as numerous references to open source tools and resources which enable the diligent reader to build MT systems for any language pair.' Target: International Journal of Translation Studies
Über das Produkt
Automatic language translation systems like those used by Google, have been revolutionized by recent advances in the methods used in statistical machine translation. This first textbook on the topic explains these innovations carefully and shows the reader, whether a student or a developer, how to build their own translation system.Alle Produktbeschreibungen
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Die hilfreichsten Kundenrezensionen auf Amazon.com
When I bought this book, I was finishing my own MT decoder and starting to
build a rudimentary IBM word alignment model trainer.
This book greatly contributed to the project in that it deeply corrected my wrong understanding of
many concepts such as dynamic programming, optimization, beam search, and etc.
Best part : It includes easy-to-understand pseudo-code for IBM 1~5 word alignment process.
It was also helpful in improving the performance of existing decoder.
As one of the leading figures in well-known Moses project and Euro Matrix,
author's explanation is firmly grounded upon practical experience and
includes a lot of elements required for building a prototype MT system.
I believe reading this book with the background knowledge
that you can learn in such books as Artificial Intelligence
: A Modern Approach or Mitchell's Machine Learning,
may maximize your learning rate, since the subject stuffs in
these books are highly inter-related with each others,
for example, unsupervised learning algorithm(especially EM),
optimization and search.
This book is top-ranked in NLP category of my personal book shelf.
I guess you won't regret if you purchase one.
Koehn has the ability to take complex statistical concepts and make them comprehensible. And he has an encyclopedic knowledge of the state-of-the-art in SMT. His bibliography alone is worth the price of this book.
This book will be the gold standard in SMT for years to come. I would highly recommend to students and professionals in the field.