| 疾病命名短语识别的最大熵方法 |
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| 引用本文： | 蔡晓白,樊孝忠.疾病命名短语识别的最大熵方法[J].北京理工大学学报,2006(5):517-520. |
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| 作者姓名： | 蔡晓白 樊孝忠 |
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| 作者单位： | 北京理工大学计算机科学技术学院,北京100081;北京理工大学计算机科学技术学院,北京100081 |
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| 基金项目： | 教育部博士学科点专项科研基金资助课题(20050007023) |
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| 摘 要： | 提出一种基于最大熵模型的中文疾病命名短语识别方法,在模型特征选择上,将领域本体信息作为模型的一种特征.由此实现的疾病命名短语识别分类器具备有监督学习和利用领域知识的能力.实验结果表明,对于疾病命名短语识别的准确率达到89.7%,召回率87.6%,F-评价值88.64%.
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| 关 键 词： | 最大熵模型 特征选择 本体 疾病命名短语识别 |
| 收稿时间： | 2005/10/27 0:00:00 |
Maximum Entropy Method in Recognizing Disease Named Phrase in Chinese |
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| CAI Xiao-bai and FAN Xiao-zhong.Maximum Entropy Method in Recognizing Disease Named Phrase in Chinese[J].Journal of Beijing Institute of Technology(Natural Science Edition),2006(5):517-520. |
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| Authors: | CAI Xiao-bai and FAN Xiao-zhong |
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| Institution: | School of Computer Science and Technology,Beijing Institute of Technology,Beijing 100081,China;School of Computer Science and Technology,Beijing Institute of Technology,Beijing 100081,China |
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| Abstract: | A method of recognizing disease named phrase in Chinese is proposed,based on a maximum entropy model.In the feature selection,domain ontology information is utilized as a kind of feature.With the suggested method,the disease named phrase recognition classifier has supervised learning(ability) and the ability of assimilating and utilizing domain knowledge.Experimental results showed a precision of recognition for disease named phrase at 89.7%,a recall of 87.6% and a F-measure of 88.64%. |
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| Keywords: | maximum entropy model feature selection ontology disease named phrase recognition |
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