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2018 | OriginalPaper | Chapter

Learning Bilingual Lexicon for Low-Resource Language Pairs

Authors : ShaoLin Zhu, Xiao Li, YaTing Yang, Lei Wang, ChengGang Mi

Published in: Natural Language Processing and Chinese Computing

Publisher: Springer International Publishing

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Abstract

Learning bilingual lexicon from monolingual data is a novel idea in natural language process which can benefit many low-resource language pairs. In this paper, we present an approach for obtaining bilingual lexicon from monolingual data. Our method only requires a small seed bilingual lexicon and we use the Canonical Correlation Analysis to construct a shared latent space to explain two monolingual embeddings how to be linked. Experimental results show that a considerable precision and size bilingual lexicon can be learned in Chinese-Uyghur and Chinese-Kazakh monolingual data.

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previous chapter Using NMT with Grammar Information and Self-taught Mechanism in Translating Chinese Symptom and Disease Terminologies

next chapter Exploring the Impact of Linguistic Features for Chinese Readability Assessment

Word2vec: https://code.google.com/p/word2vec/.

We omitted the derivation process, if you want to learn more, the work of Bach and Jordan (2005) is good for you.

Scrapy: https://pypi.python.org/pypi/Scrapy/1.4.0.

OpenCC: https://pypi.python.org/pypi/opencc-python/.

Jieba: https://pypi.python.org/pypi/jieba/.

BilBOWA: https://github.com/gouwsmeister/bilbowa.

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Title: Learning Bilingual Lexicon for Low-Resource Language Pairs
Authors: ShaoLin Zhu
Xiao Li
YaTing Yang
Lei Wang
ChengGang Mi
Publisher: Springer International Publishing
Book: Natural Language Processing and Chinese Computing
Print ISBN: 978-3-319-73617-4

Electronic ISBN: 978-3-319-73618-1

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-319-73618-1_66

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