Hybrid Approaches to Machine Translation by Marta R. Costa-jussHybrid Approaches to Machine Translation by Marta R. Costa-juss

Hybrid Approaches to Machine Translation

byMarta R. Costa-jussEditorReinhard Rapp, Patrik Lambert

Hardcover | July 21, 2016

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This volume provides an overview of the field of Hybrid Machine Translation (MT) and presents some of the latest research conducted by linguists and practitioners from different multidisciplinary areas. Nowadays, most important developments in MT are achieved by combining data-driven and rule-based techniques. These combinations typically involve hybridization of different traditional paradigms, such as the introduction of linguistic knowledge into statistical approaches to MT, the incorporation of data-driven components into rule-based approaches, or statistical and rule-based pre- and post-processing for both types of MT architectures.

The book is of interest primarily to MT specialists, but also - in the wider fields of Computational Linguistics, Machine Learning and Data Mining - to translators and managers of translation companies and departments who are interested in recent developments concerning automated translation tools.

Marta R. Costa-jussà  is a MarieCurie fellow at the Universitat Politècnica de Catalunya (UPC, Barcelona). Her research expertise is in machine translation and automatic speech recognition, including both experience in hybrid methodologies and scarce resources. She received her PhD from the UPC in 2008.She has worked at LIMSI-CNRS, Uni...
Title:Hybrid Approaches to Machine TranslationFormat:HardcoverDimensions:205 pagesPublished:July 21, 2016Publisher:Springer-Verlag/Sci-Tech/TradeLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:3319213105

ISBN - 13:9783319213101


Table of Contents

Preface.- Foreword.- Chapter 1. Hybrid Machine Translation Overview.- Part 1: Adding Linguistics into SMT.- Chapter 2.  Controllent Ascent: Imbuing Statistical MT with Linguistic knowledge.- Chapter 3.  Hybrid Word Alignment.- Chapter 4.  Syntax in SMT.- Part 2. Using Machine Learning in MT.- Chapter 5.  Machine Learning in RBMT.- Chapter 6. Language-Independent Hybrid MT.- Part 3.  Hybrid NLP tools useful for MT.- Chapter 7. Use of Dependency Parsers in MT.- Chapter 8.  Word Sense Disambiguation in MT.


Editorial Reviews

"As the chapters are mostly self-contained, the book can be useful for a wide range of readers. It is primarily devoted to MT specialists in wider fields like computational linguistics and machine learning. It is also useful for 'translators and managers of translation companies and departments who are interested in recent developments concerning automated translation tools.' It could also be useful for university teachers and students for courses that are devoted to NLP." (M. Ivanovic, Computing Reviews, January, 2017)