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Enabling Medical Translation for Low-Resource Languages Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Bilingual Contexts from Comparable Corpora to Mine for Translations of Collocations

Authors : Shiva Taslimipoor, Ruslan Mitkov, Gloria Corpas Pastor, Afsaneh Fazly

Published in: Computational Linguistics and Intelligent Text Processing

Publisher: Springer International Publishing

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Abstract

Due to the limited availability of parallel data in many languages, we propose a methodology that benefits from comparable corpora to find translation equivalents for collocations (as a specific type of difficult-to-translate multi-word expressions). Finding translations is known to be more difficult for collocations than for words. We propose a method based on bilingual context extraction and build a word (distributional) representation model drawing on these bilingual contexts (bilingual English-Spanish contexts in our case). We show that the bilingual context construction is effective for the task of translation equivalent learning and that our method outperforms a simplified distributional similarity baseline in finding translation equivalents.

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previous chapter Genetic-Based Decoder for Statistical Machine Translation
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Footnotes
1
We use the lexicon built by applying GIZA++ on the Spanish–English portion of the Europarl.
 
2
The software is available in the websites of the authors of [13].
 
3
We set the frequency threshold to 10 in our experiments.
 
10
The comparable corpora that we prepared is available on https://​github.​com/​shivaat/​EnEsCC.
 
11
Note that we add noise in both Spanish–English and English–Spanish directions.
 
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Metadata
Title
Bilingual Contexts from Comparable Corpora to Mine for Translations of Collocations
Authors
Shiva Taslimipoor
Ruslan Mitkov
Gloria Corpas Pastor
Afsaneh Fazly
Copyright Year
2018
Book
Computational Linguistics and Intelligent Text Processing

Print ISBN: 978-3-319-75486-4

Electronic ISBN: 978-3-319-75487-1

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-75487-1_10

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