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中文摘要：
汉语词法分析是中文自然语言处理的首要任务．文中深入研究中文分词、词性标注、命名实体识别所面临的问题及相互之间的协作关系，并阐述了一个基于混合语言模型构建的实用汉语词法分析系统．该系统采用了多种语言模型，有针对性地处理词法分析所面临的各个问题．其中分词系统参加了2005年第二届国际汉语分词评测，在微软亚洲研究院、北京大学语料库开放测试中，分别获得F量度为97．2％与96．7％．而在北京大学标注的《人民日报》语料库的开放评测中，词性标注获得96．1％的精确率，命名实体识别获得的F量度值为88．6％．
英文摘要：
Chinese lexical analysis is the foundation task for most Chinese natural language processing. In this paper, word segmentation, POS tagging, named entity recognition and their relation are well discussed. Moreover, a pragmatic lexical analysis system based on mixed language models is presented, which adopts many models, such as n-gram, hidden Markov model, maximum entropy model, support vector machine and conditional random fields, they have good performance in the special sub-tasks. The Word Segmenter participated in the Second International Chinese Word Segmentation Bakeoff in 2005, and achieved 97.2% and 96.7%in terms of F- measure in MSR and PKU open test respectively. While the POS tagging and named entity recognition modules achieved 96.1 % in precision and 88. 6 % in F-measure respectively in open test with the corpus that came from six-month corpora of Chinese Peoples＇ Daily.
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