Abstract:

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, superordinates into Chinese corpus by the weakly supervised learning extension approach and other methods, thus Thai word distribution in crosslingual environment of Chinese and Thai 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 Thai. 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 word distribution representations;neural probabilistic language model