Machine Translation with Minimal Reliance on Parallel Resources

• Κύριοι συγγραφείς: Tambouratzis, George (Συγγραφέας), Vassiliou, Marina (Συγγραφέας), Sofianopoulos, Sokratis (Συγγραφέας)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2017.
• Σειρά: SpringerBriefs in Statistics,
• Θέματα: Computer science.; Mathematical statistics.; Computational linguistics.; Pattern recognition.; Statistics.; Computer Science.; Language Translation and Linguistics.; Probability and Statistics in Computer Science.; Pattern Recognition.; Statistics and Computing/Statistics Programs.

 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. 

 Preliminaries -- Implementation -- Main translation process -- Assessing PRESEMT -- Expanding the system -- Extensions to the PRESEMT methodology -- Conclusions and future work -- References.