| 词性标注与动词细分类研究 |
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| 引用本文： | 尹木,肖铮. 词性标注与动词细分类研究[J]. 数字社区&智能家居, 2009, 0(24) |
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| 作者姓名： | 尹木 肖铮 |
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| 作者单位： | 成都东软信息技术职业学院数字媒体教研室;成都东软信息技术职业学院教务部; |
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| 摘 要： | 动词细分类和词性标注有些类似,它是在词性标注基础上对其中的动词进行更细致的类别标注。根据动词细分类自身的特点,提出了一种改进的隐马尔科夫模型的方法进行动词类别的自动划分,再通过与最大熵的方法进行比较,证明这种方法取得了较高的准确率。
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| 关 键 词： | 动词细分类 词性标注 隐马尔科夫模型 最大熵 |
The Research on Part of Speech Tagging and Verb Subdivision |
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| YIN Mu,XIAO Zheng. The Research on Part of Speech Tagging and Verb Subdivision[J]. Digital Community & Smart Home, 2009, 0(24) |
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| Authors: | YIN Mu XIAO Zheng |
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| Affiliation: | 1.Digital Meadia Staff;Chengdu Neusoft Vocational Institute of Information Technology;Chengdu 611844;China;2.Ministry of Educational Administration;China |
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| Abstract: | Verb subdivision is similar to part of speech tagging. It subdivides verbs into more detailed classes based on the result of part of speech tagging. According to the specialty of verb subdivision, it introduces a method of improved HMM to subdivide verbs. By comparing with the method of Maximum Entropy, it proves that this method is a high precision. |
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| Keywords: | part of speech tagging verb subdivision hidden Marko model maximum entropy |
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