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Linguistically Motivated Statistical Machine Translation

Models and Algorithms

  • Book
  • © 2015

Overview

Authors:
  1. Deyi Xiong

    1. Soochow University, Suzhou, China

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    You can also search for this author in PubMed   Google Scholar

  2. Min Zhang

    1. Soochow University, Suzhou, China

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    You can also search for this author in PubMed   Google Scholar

  • 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
  • Includes supplementary material: sn.pub/extras

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Table of contents (9 chapters)

  1. Front Matter

    Pages i-xii
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  2. Introduction

    • Deyi Xiong, Min Zhang
    Pages 1-13

  3. BTG-Based SMT

    • Deyi Xiong, Min Zhang
    Pages 15-42

  4. Syntactically Annotated Reordering

    • Deyi Xiong, Min Zhang
    Pages 43-70

  5. Semantically Informed Reordering

    • Deyi Xiong, Min Zhang
    Pages 71-79

  6. Lexicalized Bracketing

    • Deyi Xiong, Min Zhang
    Pages 81-92

  7. Linguistically Motivated Bracketing

    • Deyi Xiong, Min Zhang
    Pages 93-106

  8. Translation Rule Selection with Document-Level Semantic Information

    • Deyi Xiong, Min Zhang
    Pages 107-124

  9. Translation Error Detection with Linguistic Features

    • Deyi Xiong, Min Zhang
    Pages 125-135

  10. Closing Remarks

    • Deyi Xiong, Min Zhang
    Pages 137-140

  11. Back Matter

    Pages 141-152
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Keywords

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.

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)

Authors and Affiliations

  • Soochow University, Suzhou, China

    Deyi Xiong, Min Zhang

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.

Bibliographic Information

  • Book Title: Linguistically Motivated Statistical Machine Translation

  • Book Subtitle: Models and Algorithms

  • Authors: Deyi Xiong, Min Zhang

  • DOI: https://doi.org/10.1007/978-981-287-356-9

  • Publisher: Springer Singapore

  • eBook Packages: Humanities, Social Sciences and Law, Social Sciences (R0)

  • Copyright Information: Springer Science+Business Media Singapore 2015

  • Hardcover ISBN: 978-981-287-355-2Published: 16 March 2015

  • Softcover ISBN: 978-981-10-1365-2Published: 09 October 2016

  • eBook ISBN: 978-981-287-356-9Published: 11 February 2015

  • Edition Number: 1

  • Number of Pages: XII, 152

  • Number of Illustrations: 52 b/w illustrations

  • Topics: Computational Linguistics

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