Khmer-Chinese bilingual LDA topic model based on dictionary

Abstract

Multilingual probabilistic topic models have been widely used in topic of mining area in multilingual documents, this paper proposes the Khmer-Chinese bilingual latent Dirichlet allocation (KCB-LDA) model based on the bilingual dictionary. With the bilingual attribute of entries in dictionary, this method first maps the words expressing same semantic meaning to the concept abstract layer, then group concepts into the same topic space. Finally, documents in different languages will share the same latent topics. The same topics can be represented in both Chinese and Khmer jointly when given a bilingual corpus by the introduction of the concept layer. The experimental results show that our topic modelling approach has better predictive power.