Abstract
Bilingual word embedding, which maps word embedding of two languages into one vector space, has been widely applied in the domain of machine translation, word sense disambiguation and so on. However, no model has been universally accepted for learning bilingual word embedding. In this work, we propose a novel model named CJ-BOC to learn Chinese-Japanese word embeddings. Given Chinese and Japanese share a large portion of common characters, we exploit them in our training process. We demonstrated the effectiveness of such exploitation through theoretical and also experimental study. To evaluate the performance of CJ-BOC, we conducted a comprehensive experiment, which reveals its speed advantage, and high quality of acquired word embeddings as well.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
https://code.google.com/archive/p/word2vec, accessed date: March 17, 2016.
- 2.
http://www.statmt.org/moses/giza/GIZA++.html, accessed date: June 11, 2016.
- 3.
http://taku910.github.io/mecab, accessed date: May 12, 2016.
- 4.
https://github.com/fxsjy/jieba, accessed date: August 2, 2015.
- 5.
Our source code is available at https://github.com/jileiwang/cjboc.
References
Hinton, G.E.: Learning distributed representations of concepts. In: Proceedings of the Eighth Annual Conference of the Cognitive Science Society
Mikolov, T., Le, Q.V., Sutskever, I.: Exploiting similarities among languages for machine translation (2013). arXiv:1309.4168
Guo, J., Che, W., Wang, H., Liu, T.: Learning sense-specific word embeddings by exploiting bilingual resources. In: Proceedings of COLING, pp. 497–507 (2014)
Bengio, Y., Ducharme, R., Vincent, P., Janvin, C.: A neural probabilistic language model. J. Mach. Learn. Res. 3, 1137–1155 (2003)
Mnih, A., Hinton, G.E.: A scalable hierarchical distributed language model. In: Advances in Neural Information Processing Systems, pp. 1081–1088 (2009)
Mikolov, T., Yih, W.T., Zweig, G.: Linguistic regularities in continuous space word representations. In: HLT-NAACL, pp. 746–751 (2013)
Pennington, J., Socher, R., Manning, C.: Glove: global vectors for word representation. In: Conference on Empirical Methods in Natural Language Processing (2014)
Gouws, S., Bengio, Y., Corrado, G.: BilBOWA: fast bilingual distributed representations without word alignments (2014). arXiv:1410.2455
Chen, X., Xu, L., Liu, Z., Sun, M., Luan, H.: Joint learning of character and word embeddings. In: International Conference on Artificial Intelligence. AAAI Press (2015)
Chu, C., Nakazawa, T., Kurohashi, S.: Constructing a Chinese-Japanese parallel corpus from Wikipedia. In: Proceedings of the Ninth Conference on International Language Resources and Evaluation (LREC 2014), Reykjavik, Iceland, May 2014
Chu, C., Nakazawa, T., Kurohashi, S.: Chinese characters mapping table of Japanese, traditional Chinese and simplified Chinese. In: Proceedings of the Eighth Conference on International Language Resources and Evaluation (LREC 2012), pp. 2149–2152, Istanbul, Turkey, May 2012
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv:1301.3781
Veale, T.: An analogy-oriented type hierarchy for linguistic creativity. Knowl. Based Syst. 19(7), 471–479 (2006)
Veale, T., Li, G.: Analogy as an organizational principle in the construction of large knowledge-bases. In: Prade, H., Richard, G. (eds.) Computational Approaches to Analogical Reasoning: Current Trends. SCI, vol. 548, pp. 83–101. Springer, Heidelberg (2014). doi:10.1007/978-3-642-54516-0_4
Acknowledgement
This research is supported in part by the Major State Basic Research Development Program of China (973 Program, 2012CB315803), the National Natural Science Foundation of China (61371078), and the Research Fund for the Doctoral Program of Higher Education of China (20130002110051).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Wang, J., Luo, S., Li, Y., Xia, ST. (2016). Learning Chinese-Japanese Bilingual Word Embedding by Using Common Characters. In: Lehner, F., Fteimi, N. (eds) Knowledge Science, Engineering and Management. KSEM 2016. Lecture Notes in Computer Science(), vol 9983. Springer, Cham. https://doi.org/10.1007/978-3-319-47650-6_7
Download citation
DOI: https://doi.org/10.1007/978-3-319-47650-6_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-47649-0
Online ISBN: 978-3-319-47650-6
eBook Packages: Computer ScienceComputer Science (R0)