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Linguistically Motivated Statistical Machine Translation

Models and Algorithms

Authors: Xiong, Deyi, Zhang, Min

  • Provides solutions for open problems concerning the integration of linguistic knowledge into SMT
  • Helps readers to better understand the effects and impacts of linguistic knowledge on machine translation
  • Promotes the often-ignored bracketing model (or phrase segmentation model) to the SMT community
  • Elaborates on the entire framework of BTG-based SMT formalism
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eBook $99.00
price for USA (gross)
  • ISBN 978-981-287-356-9
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $129.00
price for USA
  • ISBN 978-981-287-355-2
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $129.00
price for USA
  • Customers within the U.S. and Canada please contact Customer Service at 1-800-777-4643, Latin America please contact us at +1-212-460-1500 (Weekdays 8:30am – 5:30pm ET) to place your order.
  • Due: November 17, 2016
  • ISBN 978-981-10-1365-2
  • Free shipping for individuals worldwide
About this book

This book provides a wide variety of algorithms and models to integrate linguistic knowledge into Statistical Machine Translation (SMT). It helps advance conventional SMT to linguistically motivated SMT by enhancing the following three essential components: translation, reordering and bracketing models. It also serves the purpose of promoting the in-depth study of the impacts of linguistic knowledge on machine translation. Finally it provides a systematic introduction of Bracketing Transduction Grammar (BTG) based SMT, one of the state-of-the-art SMT formalisms, as well as a case study of linguistically motivated SMT on a BTG-based platform.

About the authors

Deyi Xiong is a professor at Soochow University. Previously he was a research scientist at the Institute for Infocomm Research of Singapore from 2007-2013. He completed his Ph.D. in Computer Science at the Institute of Computing Technology of Chinese Academy of Sciences in 2007. His research interests are in the area of natural language processing, including parsing and statistical machine translation.

Min Zhang is a professor at Soochow University. He obtained his Ph.D. degree in Computer Science at Harbin Institute of Technology in 1997. His research interests include machine translation, natural language processing and text mining.

Reviews

“Linguistically Motivated Statistical Machine Translation, written by Deyi Xiong and Min Zhang is an overview of (mostly) already published work by the same researchers, rewritten into a coherent book that explains how several different research aspects fit into one research paradigm. … the book is inspiring and worth reading, if you wish to try out and improve your SMT system.” (Vincent Vandeghinste, Machine Translation, Vol. 29, 2015)


Table of contents (9 chapters)

Buy this book

eBook $99.00
price for USA (gross)
  • ISBN 978-981-287-356-9
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $129.00
price for USA
  • ISBN 978-981-287-355-2
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $129.00
price for USA
  • Customers within the U.S. and Canada please contact Customer Service at 1-800-777-4643, Latin America please contact us at +1-212-460-1500 (Weekdays 8:30am – 5:30pm ET) to place your order.
  • Due: November 17, 2016
  • ISBN 978-981-10-1365-2
  • Free shipping for individuals worldwide
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Bibliographic Information

Bibliographic Information
Book Title
Linguistically Motivated Statistical Machine Translation
Book Subtitle
Models and Algorithms
Authors
Copyright
2015
Publisher
Springer Singapore
Copyright Holder
Springer Science+Business Media Singapore
eBook ISBN
978-981-287-356-9
DOI
10.1007/978-981-287-356-9
Hardcover ISBN
978-981-287-355-2
Softcover ISBN
978-981-10-1365-2
Edition Number
1
Number of Pages
XII, 152
Number of Illustrations and Tables
52 b/w illustrations
Topics