| Issue |
MATEC Web Conf.
Volume 100, 2017
13th Global Congress on Manufacturing and Management (GCMM 2016)
|
|
|---|---|---|
| Article Number | 02054 | |
| Number of page(s) | 6 | |
| Section | Part 2: Internet +, Big data and Flexible manufacturing | |
| DOI | https://doi.org/10.1051/matecconf/201710002054 | |
| Published online | 08 March 2017 | |
The Distribution of Words in Chinese and Laos Based on Cross Language Corpus
1 School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China
2 Key Laboratory of Intelligent Information Processing, Kunming University of Science and Technology, Kunming 650500, China
a Email: 120166881@qq.com
b Email: 915090822@qq.com
c Email: zf158@sina.com
d Email: 939127870@qq.com
Word representation is the basic research content of natural language processing. At present, distributed representation of monolingual words has shown satisfactory application effect in some Neural Probabilistic Language (NPL) research, while as for distributed representation of cross-lingual words, there is little research both at home and abroad. Aiming at this problem given distribution similarity of nouns and verbs in these two languages, we embed mutual translated words, synonyms, super-ordinates into Chinese corpus by the weakly supervised learning extension approach and other methods, thus Laos word distribution in cross-lingual environment of Chinese and Laos is learned. We applied the distributed representation of the cross-lingual words learned before to compute similarities of bilingual texts and classify the mixed text corpus of Chinese and Laos, Experimental results show that the proposal has a satisfactory effect on the two tasks.
Key words: Weakly supervised learning extension / cross-lingual corpus / cross-lingual words distribution representations / neural probabilistic language model
© The Authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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