Abstract: Most of existing researches mainly focus on recognizing the names of Chinese person while seldom specializing in recognizing Japanese and transliterated person names. This paper proposes a method based on CRF and combines person name reliability model and contextual rules （simply, CRRM） to recognize the person names in Chinese sentences. Partial frequency statistical algorithm is also used to revise the misrecognized boundary of names and proliferation operation is used to recall those unrecognized names with the already recognized one. Experiments based on a true dataset show that this approach is efficient in recognizing the person names from Chinese texts. The F-value for recognition of Chinese person name, Japanese name and transliterated person name is higher than 90%.

Key words: Chinese person name recognition, Conditional Random Fields（CRF） model, person name reliability model, contextual rules, marginal probability

摘要： 当前中文人名识别的研究主要针对中国人名，而对日本人名及音译人名的专门研究相对较少，识别效果也亟待提高。提出利用CRRM方法进行中、日及音译人名同步识别。该方法基于CRF（Conditional Random Fields）并结合了上下文规则及人名可信度模型。此外，利用局部统计算法对边界识别错误的人名进行修正，并利用扩散操作召回未被识别的人名。实验结果表明，中、日、音译人名识别的F值均高于90%，提出的方法可以取得较好的识别效果。

关键词: 中文人名识别, 条件随机域（CRF）模型, 人名可信度模型, 上下文规则, 边缘概率