Machine Translation

1. Auflage 2015
ISBN: 978-1-4398-9718-8
Verlag: Taylor & Francis Ltd (Sales)

Buch, Englisch, 264 Seiten, Format (B × H): 161 mm x 242 mm, Gewicht: 581 g

Three paradigms have dominated machine translation (MT)—rule-based machine translation (RBMT), statistical machine translation (SMT), and example-based machine translation (EBMT). These paradigms differ in the way they handle the three fundamental processes in MT—analysis, transfer, and generation (ATG). In its pure form, RBMT uses rules, while SMT uses data. EBMT tries a combination—data supplies translation parts that rules recombine to produce translation.

Machine Translation compares and contrasts the salient principles and practices of RBMT, SMT, and EBMT. Offering an exposition of language phenomena followed by modeling and experimentation, the text:

- Introduces MT against the backdrop of language divergence and the Vauquois triangle

- Presents expectation maximization (EM)-based word alignment as a turning point in the history of MT

- Discusses the most important element of SMT—bilingual word alignment from pairs of parallel translations

- Explores the IBM models of MT, explaining how to find the best alignment given a translation pair and how to find the best translation given a new input sentence

- Covers the mathematics of phrase-based SMT, phrase-based decoding, and the Moses SMT environment

- Provides complete walk-throughs of the working of interlingua-based and transfer-based RBMT

- Analyzes EBMT, showing how translation parts can be extracted and recombined to translate a new input, all automatically

- Includes numerous examples that illustrate universal translation phenomena through the usage of specific languages

Machine Translation is designed for advanced undergraduate-level and graduate-level courses in machine translation and natural language processing. The book also makes a handy professional reference for computer engineers.

Print Versions of this book also include access to the ebook version.

Zielgruppe

Advanced undergraduate and graduate students of machine translation and natural language processing, as well as computer engineers.

Autoren/Hrsg.

Bhattacharyya, Pushpak

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Theoretische Informatik
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Spiele-Programmierung, Rendering, Animation
• Wirtschaftswissenschaften Volkswirtschaftslehre Volkswirtschaftslehre Allgemein Wirtschaftsstatistik, Demographie
• Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsmathematik und -statistik

Inhaltsverzeichnis

Introduction. Learning Bilingual Word Mappings. IBM Model of Alignment. Phrase-Based Machine Translation. Rule-Based Machine Translation. Example-Based Machine Translation.

Über Autor(innen)

Pushpak Bhattacharyya is Vijay and Sita Vashee chair professor of computer science and engineering at the Indian Institute of Technology (IIT) Bombay, where he has been teaching and researching for the last 25 years. He was educated at IIT Kharagpur (B.Tech), IIT Kanpur (M.Tech), and IIT Bombay (Ph.D). While earning his Ph.D, he was visiting scholar at the Massachusetts Institute of Technology. Subsequently, he has been visiting professor at Stanford University and University of Grenoble, and distinguished lecturer at the University of Houston. Dr. Bhattacharyya’s research interests include natural language processing, machine learning, machine translation, information extraction, sentiment analysis, and cross-lingual search, in which he has published extensively. Currently, he is associate editor of ACM Transactions on Asian Language Information Processing and vice president-elect of Association of Computational Linguistics (ACL).