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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Notes
- 1.
Word2vec: https://code.google.com/p/word2vec/.
- 2.
We omitted the derivation process, if you want to learn more, the work of Bach and Jordan (2005) is good for you.
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- 4.
- 5.
- 6.
BilBOWA: https://github.com/gouwsmeister/bilbowa.
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Acknowledgments
This work is supported by the Xinjiang Fun under Grant (No. 2015KL031), the West Light Foundation of The Chinese Academy of Sciences (No. 2015-XBQN-B-10), the Xinjiang Science and Technology Major Project (No. 2016A03007-3) and Natural Science Foundation of Xinjiang (No. 2015211B034).
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Zhu, S., Li, X., Yang, Y., Wang, L., Mi, C. (2018). Learning Bilingual Lexicon for Low-Resource Language Pairs. In: Huang, X., Jiang, J., Zhao, D., Feng, Y., Hong, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2017. Lecture Notes in Computer Science(), vol 10619. Springer, Cham. https://doi.org/10.1007/978-3-319-73618-1_66
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DOI: https://doi.org/10.1007/978-3-319-73618-1_66
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