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Recent Advances in Example-Based Machine Translation

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Cover of 'Recent Advances in Example-Based Machine Translation'

Table of Contents

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    Book Overview
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    Chapter 1 An Overview of EBMT
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    Chapter 2 What is Example-Based Machine Translation?
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    Chapter 3 EBMT in a Controlled Environment
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    Chapter 4 EBMT Seen as Case-Based Reasoning
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    Chapter 5 Formalizing Translation Memory
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    Chapter 6 An Example-Based Machine Translation System Using DP-Matching Between Word Sequences
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    Chapter 7 A Hybrid Rule and Example-Based Method for Machine Translation
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    Chapter 8 EBMT of POS-Tagged Sentences by Recursive Division Via Inductive Learning
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    Chapter 9 Learning Translation Templates from Bilingual Translation Examples
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    Chapter 10 Clustered Transfer Rule Induction for Example-Based Translation
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    Chapter 11 Translation Patterns, Linguistic Knowledge and Complexity in an Approach to EBMT
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    Chapter 12 Inducing Translation Grammars from Bracketed Alignments
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    Chapter 13 Extracting Translation Knowledge from Parallel Corpora
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    Chapter 14 Finding Translation Patterns from Paired Source and Target Dependency Structures
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    Chapter 15 A Best-First Alignment Algorithm for Automatic Extraction of Transfer Mappings from Bilingua Corpora
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    Chapter 16 Translating with Examples: The LFG-DOT Models of Translation
Attention for Chapter 6: An Example-Based Machine Translation System Using DP-Matching Between Word Sequences
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Chapter title
An Example-Based Machine Translation System Using DP-Matching Between Word Sequences
Chapter number 6
Book title
Recent Advances in Example-Based Machine Translation
Published by
Springer, Dordrecht, January 2003
DOI 10.1007/978-94-010-0181-6_6
Book ISBNs
978-1-4020-1401-7, 978-9-40-100181-6
Authors

Eiichiro Sumita, Sumita, Eiichiro

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 1 Mendeley reader of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 1 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1 100%
Readers by discipline Count As %
Computer Science 1 100%

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