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Parallel Text Processing

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Cover of 'Parallel Text Processing'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 From the Rosetta stone to the information society
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    Chapter 2 Pattern recognition for mapping bitext correspondence
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    Chapter 3 Multilingual text alignment
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    Chapter 4 A comprehensive bilingual word alignment system
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    Chapter 5 A knowledge-lite approach to word alignment
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    Chapter 6 From sentences to words and clauses
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    Chapter 7 Bracketing and aligning words and constituents in parallel text using Stochastic Inversion Transduction Grammars
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    Chapter 8 The translation network
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    Chapter 9 Parallel text alignment using crosslingual information retrieval techniques
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    Chapter 10 Parallel alignment of structured documents
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    Chapter 11 A statistical view on bilingual lexicon extraction
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    Chapter 12 Terminology extraction from parallel technical texts
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    Chapter 13 Term alignment in use
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    Chapter 14 Automatic dictionary extraction for cross-language information retrieval
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    Chapter 15 Parallel texts in computer-assisted language learning
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    Chapter 16 Japanese-English aligned bilingual corpora
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    Chapter 17 Building a parallel corpus of English/Panjabi
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    Chapter 18 Sharing of translation memory databases derived from aligned parallel text
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    Chapter 19 Evaluation of parallel text alignment systems
Attention for Chapter 11: A statistical view on bilingual lexicon extraction
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Citations

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Chapter title
A statistical view on bilingual lexicon extraction
Chapter number 11
Book title
Parallel Text Processing
Published by
Springer, Dordrecht, January 2000
DOI 10.1007/978-94-017-2535-4_11
Book ISBNs
978-9-04-815555-2, 978-9-40-172535-4
Authors

Pascale Fung

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 32 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 4 13%
Student > Ph. D. Student 3 9%
Researcher 3 9%
Lecturer 2 6%
Student > Master 2 6%
Other 3 9%
Unknown 15 47%
Readers by discipline Count As %
Computer Science 8 25%
Linguistics 3 9%
Agricultural and Biological Sciences 1 3%
Business, Management and Accounting 1 3%
Social Sciences 1 3%
Other 1 3%
Unknown 17 53%