关键词: 双语词向量, 典型相关分析（CCA）, 汉柬双语, 多重典型相关分析算法

Abstract:

A large number of parallel bilingual texts are needed to acquire the bilingual word embedding in the existing research methods of bilingual word embedding, and there are some key problems in Khmer-Chinese bilingualism. As English is a general language, English-Chinese and English-Khmer bilingual parallel texts are more and easier to obtain. Therefore, the cross-language word embedding of canonical correlation analysis is further improved, and a method of constructing Khmer-Chinese bilingual word embedding based on multiple CCA algorithm with English as the intermediate language is proposed. The English and Chinese word embedding is projected into the Chinese-English embedding space, and the English and Khmer word embedding is projected into the Khmer-English embedding space. According to CCA algorithm, the English-Chinese and English-Khmer bilingual word embedding is obtained respectively. Then, the English-Khmer and English-Chinese bilingual word embedding obtained from the previous step are projected into the same embedding space of the third party, and the projection transformation matrix of Khmer and Chinese in the new embedding space is obtained according to CCA algorithm. Finally, the Khmer-English-Chinese multilingual word embedding is obtained. The multilingual word embedding contains the Khmer-Chinese bilingual word embedding. Compared with traditional methods, this method solves the problem of scarcity of initial Khmer-Chinese parallel texts faced by other models, and obtains higher Khmer-Chinese bilingual word embedding.

Key words: bilingual word embedding, Canonical Correlation Analysis（CCA）, Khmer-Chinese bilingual, multiple Canonical Correlation Analysis（CCA） algorithm