International Conference on Information Technology and Computer Science, 3rd (ITCS 2011)
81 Uyghur Named Entity Identification Basing on Maximum Entropy Model
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Named entity identification is a fundamental task during natural language processing. This paper puts forward identification method of Uighur which is based on maximum entropy models. Maximum entropy model can make full use of various and arbitrary language features. The language features of Chinese and English generally only integrate part of speech, word forms and other information. This paper combines with the characteristics of Uighur and makes segmentation of Uighur words; it takes word stem and word affix information as characteristics and adds to the maximum entropy model. The results of experiment show that named entity identification accuracy, recall ratio and F value have been significantly enhanced in the proposed method.