Statistical machine translation /

"The field of machine translation has recently been energized by the emergence of statistical techniques, which have brought the dream of automatic language translation closer to reality. This class-tested textbook, authored by an active researcher in the field, provides a gentle and accessible...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Koehn, Philipp
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Cambridge ; New York : Cambridge University Press, ©2010.
Temas:
Machine translating.
Traduction automatique.
LANGUAGE ARTS & DISCIPLINES > Translating & Interpreting.
Machine translating
Maschinelle Übersetzung
Sprachstatistik
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Cover
  • Half-title
  • Title
  • Copyright
  • Dedication
  • Contents
  • Preface
  • Part I Foundations
  • Chapter 1 Introduction
  • 1.1 Overview
  • 1.2 History of Machine Translation
  • 1.3 Applications
  • 1.4 Available Resources
  • 1.5 Summary
  • Chapter 2 Words, Sentences, Corpora
  • 2.1 Words
  • 2.2 Sentences
  • 2.3 Corpora
  • 2.4 Summary
  • Chapter 3 Probability Theory
  • 3.1 Estimating Probability Distributions
  • 3.2 Calculating Probability Distributions
  • 3.3 Properties of Probability Distributions
  • 3.4 Summary
  • Part II Core Methods
  • Chapter 4 Word-Based Models
  • 4.1 Machine Translation by Translating Words
  • 4.2 Learning Lexical Translation Models
  • 4.3 Ensuring Fluent Output
  • 4.4 Higher IBM Models
  • 4.5 Word Alignment
  • 4.6 Summary
  • Chapter 5 Phrase-Based Models
  • 5.1 Standard Model
  • 5.2 Learning a Phrase Translation Table
  • 5.3 Extensions to the Translation Model
  • 5.4 Extensions to the Reordering Model
  • 5.5 EM Training of Phrase-Based Models
  • 5.6 Summary
  • Chapter 6 Decoding
  • 6.1 Translation Process
  • 6.2 Beam Search
  • 6.3 Future Cost Estimation
  • 6.4 Other Decoding Algorithms
  • 6.5 Summary
  • Chapter 7 Language Models
  • 7.1 N-Gram Language Models
  • 7.2 Count Smoothing
  • 7.3 Interpolation and Back-off
  • 7.4 Managing the Size of the Model
  • 7.5 Summary
  • Chapter 8 Evaluation
  • 8.1 Manual Evaluation
  • 8.2 Automatic Evaluation
  • 8.3 Hypothesis Testing
  • 8.4 Task-Oriented Evaluation
  • 8.5 Summary
  • Part III Advanced Topics
  • Chapter 9 Discriminative Training
  • 9.1 Finding Candidate Translations
  • 9.2 Principles of Discriminative Methods
  • 9.3 Parameter Tuning
  • 9.4 Large-Scale Discriminative Training
  • 9.5 Posterior Methods and System Combination
  • 9.6 Summary
  • Chapter 10 Integrating Linguistic Information
  • 10.1 Transliteration
  • 10.2 Morphology
  • 10.3 Syntactic Restructuring
  • 10.4 Syntactic Features
  • 10.5 Factored Translation Models
  • 10.6 Summary
  • Chapter 11 Tree-Based Models
  • 11.1 Synchronous Grammars
  • 11.2 Learning Synchronous Grammars
  • 11.3 Decoding by Parsing
  • 11.4 Summary
  • Bibliography
  • Author Index
  • Index.

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