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Hybrid approaches to machine translation

Costa-jussa, Marta R.

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Hybrid approaches to machine translation

Record Type:	Electronic resources : Monograph/item
Title/Author:	Hybrid approaches to machine translation/ edited by Marta R. Costa-jussa ... [et al.].
other author:	Costa-jussa, Marta R.
Published:	Cham :Springer International Publishing : : 2016.,
Description:	ix, 205 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer eBooks
Subject:	Natural language processing (Computer science) -
Online resource:	http://dx.doi.org/10.1007/978-3-319-21311-8
ISBN:	9783319213118

Hybrid approaches to machine translation
Hybrid approaches to machine translation[electronic resource] /edited by Marta R. Costa-jussa ... [et al.]. - Cham :Springer International Publishing :2016. - ix, 205 p. :ill. (some col.), digital ;24 cm. - Theory and applications of natural language processing,2192-032X. - Theory and applications of natural language processing..

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.

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.

ISBN: 9783319213118

Standard No.: 10.1007/978-3-319-21311-8doiSubjects--Topical Terms:

565309
Natural language processing (Computer science)

LC Class. No.: QA76.9.N38

Dewey Class. No.: 006.35

Hybrid approaches to machine translation
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245 0 0 $a Hybrid approaches to machine translation $h [electronic resource] / $c edited by Marta R. Costa-jussa ... [et al.].
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490 1 $a Theory and applications of natural language processing, $x 2192-032X
505 0 $a 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.
520 $a 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.
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