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Machine translation / by Pushpak Bhattacharyya.

By: Bhattacharyya, Pushpak [author.].
Contributor(s): Taylor and Francis.
Material type: materialTypeLabelBookPublisher: Boca Raton, FL : Chapman and Hall/CRC, an imprint of Taylor and Francis, 2015Edition: First edition.Description: 1 online resource (260 pages) : 104 illustrations.Content type: text Media type: computer Carrier type: online resourceISBN: 9780429086298.Subject(s): COMPUTERS / Machine Theory | MATHEMATICS / General | Machine translating | Machine translating | Translating and interpreting -- Data processing | Translating and interpreting -- Data processing | FOREIGN LANGUAGE STUDY / Multi-Language Phrasebooks | LANGUAGE ARTS & DISCIPLINES / Alphabets & Writing Systems | LANGUAGE ARTS & DISCIPLINES / Grammar & Punctuation | LANGUAGE ARTS & DISCIPLINES / Linguistics / General | LANGUAGE ARTS & DISCIPLINES / Readers | LANGUAGE ARTS & DISCIPLINES / SpellingAdditional physical formats: Print version: : No titleDDC classification: 418/.020285 Online resources: Click here to view.
Contents:
chapter 1 Introduction -- chapter 2 Learning Bilingual Word Mappings -- chapter 3 IBM Model of Alignment -- chapter 4 Phrase-Based Machine Translation -- chapter 5 Rule-Based Machine Translation (RBMT) -- chapter 6 Example-Based Machine Translation.
Abstract: 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.
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chapter 1 Introduction -- chapter 2 Learning Bilingual Word Mappings -- chapter 3 IBM Model of Alignment -- chapter 4 Phrase-Based Machine Translation -- chapter 5 Rule-Based Machine Translation (RBMT) -- chapter 6 Example-Based Machine Translation.

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.

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