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Open Access Publications from the University of California

A Hybrid Approach Using Maximum Entropy Model and Conditional Random Fields to Identify Tibetan Person Names


Tibetan person name recognition is one of the most difficult tasks in the area of Tibetan information processing, and the effect of recognition impacts directly on the precision of Tibetan word segmentation and the performance of relative application systems, including Tibetan-Chinese machine translation, Tibetan informationretrieval, text categorization, etc. Based on the analysis of wording rules and features of Tibetan person names, this paper proposes a method which combines maximum entropy and conditional random fields to identify Tibetan person names. The experiment shows that this approach works quite well, with the value of F1-measure reaching 93.29%.

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