ENGLISH

Hybrid Approaches to Machine Translation

Book information

Year
2016
ISBN
978-3-319-21311-8, 3319213113, 978-3-319-21310-1
Language
english
Format
PDF
Filesize
6 MB (6231043 bytes)
Series
Theory and Applications of Natural Language Processing
Pages
\208
Time added
2017-10-15 16:00:00

Description

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. Front Matter ....Pages i-ix Hybrid Machine Translation Overview (Cristina España-Bonet, Marta R. Costa-jussà)....Pages 1-24 Front Matter ....Pages 25-25 Controlled Ascent: Imbuing Statistical MT with Linguistic Knowledge (William D. Lewis, Chris Quirk, Qin Gao)....Pages 27-55 Hybrid Word Alignment (Santanu Pal, Sudip Kumar Naskar)....Pages 57-75 Syntax-Based Pre-reordering for Chinese-to-Japanese Statistical Machine Translation (Dan Han, Pascual Martínez-Gómez, Yusuke Miyao)....Pages 77-108 Front Matter ....Pages 109-109 Machine Learning Applied to Rule-Based Machine Translation (Annette Rios, Anne Göhring)....Pages 111-129 Language-Independent Hybrid MT: Comparative Evaluation of Translation Quality (George Tambouratzis, Marina Vassiliou, Sokratis Sofianopoulos)....Pages 131-157 Front Matter ....Pages 159-159 Creating Hybrid Dependency Parsers for Syntax-Based MT (Nathan David Green, Zdeněk Žabokrtský)....Pages 161-190 Using WordNet-Based Word Sense Disambiguation to Improve MT Performance (Špela Vintar, Darja Fišer)....Pages 191-205

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