| 命名实体识别研究 |
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| 引用本文： | 张晓艳,王挺,陈火旺.命名实体识别研究[J].计算机科学,2005,32(4):44-48. |
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| 作者姓名： | 张晓艳 王挺 陈火旺 |
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| 作者单位： | 国防科技大学计算机学院,长沙,410073;国防科技大学计算机学院,长沙,410073;国防科技大学计算机学院,长沙,410073 |
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| 基金项目： | 国家“863”高技术研究发展计划资助(2001AA114110) |
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| 摘 要： | 命名实体识别是文本信息处理的重要基础,已经逐步成为自然语言处理的一项关键技术。其基于规则、统计、机器学习的研究方法及成果,都推动了自然语言处理研究的发展,促进了自然语言研究与应用的紧密结合。本文回顾了命名实体识别技术的发展过程,分析了主要的方法和技术,并展望了未来的发展趋势。
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| 关 键 词： | 命名实体识别(NER) 隐马尔可夫模型(HMM) 最大熵模型(ME) |
Research on Named Entity Recognition |
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| ZHANG Xiao-Yan,WANG Ting,CHEN Huo-Wang.Research on Named Entity Recognition[J].Computer Science,2005,32(4):44-48. |
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| Authors: | ZHANG Xiao-Yan WANG Ting CHEN Huo-Wang |
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| Affiliation: | ZHANG Xiao-Yan,WANG Ting,CHEN Huo-Wang Department of Computer,National University of Defense Technology,Changsha 4l0073 |
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| Abstract: | Named Entity Recognition (NER), as the important foundation of Text Processing, has become a key technology of Natural Language Processing (NLP). It has made a great impetus to the research of NLP with its method and result. Moreover, NER has built a more effective connection between NLP theories and applications. This paper will review the history of the research, analyze the methods and technologies and give a view of the future of NER. |
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| Keywords: | Named entity recognition (NER) Hidden markov model (HMM) Maximum entropy model (ME) |
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