Tibetan-Chinese cross language named entity extraction based on comparable corpus and naturally annotated resources

Abstract

Tibetan-Chinese named entity extraction can effectively improve the performance of Tibetan-Chinese cross language question answering system, information retrieval, machine translation and other researches. In the condition of no practical Tibetan named entity recognition system and Tibetan-Chinese translation model, this paper proposes a method to extract Tibetan-Chinese entities based on comparable corpus and naturally annotated resources from webs. The main work of this paper is in the following: (1) Tibetan-Chinese comparable corpus construction. (2) Combining sentence length, word matching and boundary term features, using multi-feature fusion algorithm to obtain parallel sentences from comparable corpus. (3) Tibetan-Chinese entity mapping based on the maximum word continuous intersection model of parallel sentence. Finally, the experimental results show that our approach can effectively find Tibetan-Chinese cross language named entity.