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Machine Translation with Minimal Reliance on Parallel Resources [electronic resource] / by George Tambouratzis, Marina Vassiliou, Sokratis Sofianopoulos.

By: Contributor(s): Material type: TextTextSeries: SpringerBriefs in StatisticsPublisher: Cham : Springer International Publishing : Imprint: Springer, 2017Description: IX, 88 p. 17 illus. online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9783319631073
Subject(s): Additional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification:
  • 006.35 23
LOC classification:
  • P98-98.5
Online resources:
Contents:
Preliminaries -- Implementation -- Main translation process -- Assessing PRESEMT -- Expanding the system -- Extensions to the PRESEMT methodology -- Conclusions and future work -- References.
In: Springer eBooksSummary: 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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Preliminaries -- Implementation -- Main translation process -- Assessing PRESEMT -- Expanding the system -- Extensions to the PRESEMT methodology -- Conclusions and future work -- References.

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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