Chinese-English term translation mining based on semantic prediction

Abstract

Using abundant Web resources to mine Chinese term translations can be applied in many fields such as reading/writing assistant, machine translation and cross-language information retrieval. In mining English translations of Chinese terms, how to obtain effective Web pages and evaluate translation candidates are two challenging issues. In this paper, the approach based on semantic prediction is first proposed to obtain effective Web pages. The proposed method predicts possible English meanings according to each constituent unit of Chinese term, and expands these English items using semantically relevant knowledge for searching. The refined related terms are extracted from top retrieved documents through feedback learning to construct a new query expansion for acquiring more effective Web pages. For obtaining a correct translation list, a translation evaluation method in the weighted sum of multi-features is presented to rank these candidates estimated from effective Web pages. Experimental results demonstrate that the proposed method has good performance in Chinese-English term translation acquisition, and achieves 82.9% accuracy.