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Machine Translation with Minimal Reliance on Parallel Resources

  • 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?

Book

Part of the SpringerBriefs in Statistics book series (BRIEFSSTATIST)

Table of contents

  1. Front Matter
    Pages i-ix
  2. George Tambouratzis, Marina Vassiliou, Sokratis Sofianopoulos
    Pages 1-10
  3. George Tambouratzis, Marina Vassiliou, Sokratis Sofianopoulos
    Pages 11-28
  4. George Tambouratzis, Marina Vassiliou, Sokratis Sofianopoulos
    Pages 29-41
  5. George Tambouratzis, Marina Vassiliou, Sokratis Sofianopoulos
    Pages 43-53
  6. George Tambouratzis, Marina Vassiliou, Sokratis Sofianopoulos
    Pages 55-61
  7. George Tambouratzis, Marina Vassiliou, Sokratis Sofianopoulos
    Pages 63-75
  8. George Tambouratzis, Marina Vassiliou, Sokratis Sofianopoulos
    Pages 77-85
  9. Back Matter
    Pages 87-88

About this book

Introduction

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.​

Keywords

MT portability to new language pairs Machine learning methods Machine translation (MT) Non-reliance on expensive resources Statistical methods for language processing Pattern recognition Applied linguistics Natural language processing

Authors and affiliations

  1. 1.Institute for Language and Speech ProcessingAthensGreece
  2. 2.Institute for Language and Speech ProcessingAthensGreece
  3. 3.Institute for Language and Speech ProcessingAthensGreece

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. 

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 system for 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