| 基于两类统计机器学习模型的中文化学物质名称识别研究 |
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| 引用本文： | 潘国巍,吉久明,李楠,郑荣廷.基于两类统计机器学习模型的中文化学物质名称识别研究[J].现代情报,2011,31(11):163-165. |
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| 作者姓名： | 潘国巍 吉久明 李楠 郑荣廷 |
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| 作者单位： | 华东理工大学科技信息研究所,上海200237 |
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| 摘 要： | 与基于词典和基于规则的识别方法相比,统计机器学习方法更加适合被应用到命名实体的识别工作中来。本文主要在中文化学物质名称的识别工作中,考察两类统计机器学习模型识别效果及识别效率的优劣,实验结果表明,在所取训练语料与测试语料相同的情况下,以CRF模型为代表的条件概率模型可以展现出更好的实验性能。
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| 关 键 词： | 中文化学物质名称 条件随机场 支持向量机 识别效果 识别效率 |
Research on Recognition of Chinese Chemical Substance Names Based on Two Kinds of Machine Learning Method |
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| Authors: | Pan Guowei Ji Jiuming Li Nan Zheng Rongting |
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| Institution: | Institute of Science and Technology Information,East China University of Science and Technology, Shanghai 200237,China |
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| Abstract: | Comparing with the recognition methods based on dictionary or rule,the method based on machine learning is suitably to be applied to the research on NER(named entity recognition).This article mainly evaluated the performance of two kinds of machine learning methods SVM and CRF in the course of recognizing Chinese chemical substance names,and the result of the experiment showed:in the condition of selecting same training sample and testing sample,the conditional models(Take the CRF for example)reveal the bet... |
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| Keywords: | CRF Chinese chemical substance names labeled on char labeled on word Quantity of feature |
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