Overview
- Introduces a novel language-independent method for developing Machine Translation (MT) systems
- Includes various experiments and comparisons to other MT systems
- Provides a detailed presentation of the methodology principles and system architecture?
- Includes supplementary material: sn.pub/extras
Part of the book series: SpringerBriefs in Statistics (BRIEFSSTATIST)
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About this book
This book provides a unified view on a new methodology for Machine Translation (MT). This methodology extracts information from widely available resources (extensive monolingual corpora) while only assuming the existence of a very limited parallel corpus, thus having a unique starting point to Statistical Machine Translation (SMT). In this book, a detailed presentation of the methodology principles and system architecture is followed by a series of experiments, where the proposed system is compared to other MT systems using a set of established metrics including BLEU, NIST, Meteor and TER. Additionally, a free-to-use code is available, that allows the creation of new MT systems. The volume is addressed to both language professionals and researchers. Prerequisites for the readers are very limited and include a basic understanding of the machine translation as well as of the basic tools of natural language processing.​
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Table of contents (7 chapters)
Authors and Affiliations
About the authors
George Tambouratzis graduated from the Electrical Engeneering Department of the National Technical University of Athens (1989), and received his M.Sc. (1990) and Ph.D. (1993) degrees from Brunel University. Since 1996 he has been with the Institute for Language and Speech Processing (ILSP), working on machine learning, neural networks and evolutionary computation algorithms for computational linguistics. He is the Director of Research at ILSP and the Head of the Machine Translation Department. He co-ordinated several EU-funded projects.Â
Marina Vassiliou studied Linguistics and holds a Master’s degree in Generative Syntax from the University of Athens. As a research associate at ILSP since 2000 she has worked on various, mainly European, research projects concerning specifications for syntactic analysis, machine translation, stylometry, controlled languages, multilingual thesauri and business ontologies as well as the development of a coreference resolution systemfor Greek language.
Sokratis Sofianopoulos graduated from the University of Ioannina in 2002 and holds a M.Sc. from Heriot-Watt University (2003) and a PhD from the National Technical University of Athens (2010). Since 2005 he is a research associate at ILSP. He has worked in several European R&D programs in the field of NLP and machine translation, such as METIS-II (FP6-IST-003768), PRESEMT (FP7-ICT-248307), QTLaunchPad (FP7-ICT-296347).
Marina Vassiliou studied Linguistics and holds a Master’s degree in Generative Syntax from the University of Athens. As a research associate at ILSP since 2000 she has worked on various, mainly European, research projects concerning specifications for syntactic analysis, machine translation, stylometry, controlled languages, multilingual thesauri and business ontologies as well as the development of a coreference resolution systemfor Greek language.
Sokratis Sofianopoulos graduated from the University of Ioannina in 2002 and holds a M.Sc. from Heriot-Watt University (2003) and a PhD from the National Technical University of Athens (2010). Since 2005 he is a research associate at ILSP. He has worked in several European R&D programs in the field of NLP and machine translation, such as METIS-II (FP6-IST-003768), PRESEMT (FP7-ICT-248307), QTLaunchPad (FP7-ICT-296347).
Bibliographic Information
Book Title: Machine Translation with Minimal Reliance on Parallel Resources
Authors: George Tambouratzis, Marina Vassiliou, Sokratis Sofianopoulos
Series Title: SpringerBriefs in Statistics
DOI: https://doi.org/10.1007/978-3-319-63107-3
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: The Author(s) 2017
Softcover ISBN: 978-3-319-63105-9Published: 05 September 2017
eBook ISBN: 978-3-319-63107-3Published: 09 August 2017
Series ISSN: 2191-544X
Series E-ISSN: 2191-5458
Edition Number: 1
Number of Pages: IX, 88
Number of Illustrations: 17 b/w illustrations
Topics: Natural Language Processing (NLP), Probability and Statistics in Computer Science, Pattern Recognition, Statistics and Computing/Statistics Programs