| 基于统计的介词短语边界识别研究 |
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| 引用本文： | 张坤丽,韩英杰,昝红英,袁应成.基于统计的介词短语边界识别研究[J].河南大学学报(自然科学版),2011,41(6). |
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| 作者姓名： | 张坤丽 韩英杰 昝红英 袁应成 |
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| 作者单位： | 郑州大学信息工程学院,郑州,450001 |
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| 基金项目： | 国家自然科学基金资助项目(60970083); 北京大学计算语言学教育部重点实验室开放课题基金资助项目(KLCL-1004); 河南省科技创新人才杰出青年基金项目(104100510026) |
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| 摘 要： | 以已经分词并进行了词性标注和介词短语标注的《人民日报》为实验语料,选取其中出现频次高于20次的61个介词为实验对象,采用支持向量机、最大熵和条件随机场这3种统计模型,对介词短语边界识别进行了研究.实验结果表明在3种模型中,采用条件随机场模型效果最好,微平均准确率达到了95.68%.
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| 关 键 词： | 介词短语 支持向量机 最大熵 条件随机场 |
Prepositional Phrase Boundary Identification Based on Statistical Models |
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| ZHANG Kun-li,HAN Ying-jie,ZAN Hong-ying,YUAN Ying-cheng.Prepositional Phrase Boundary Identification Based on Statistical Models[J].Journal of Henan University(Natural Science),2011,41(6). |
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| Authors: | ZHANG Kun-li HAN Ying-jie ZAN Hong-ying YUAN Ying-cheng |
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| Institution: | ZHANG Kun-li,HAN Ying-jie,ZAN Hong-ying,YUAN Ying-cheng(College of Information Engineering,Zhengzhou University,Zhengzhou 450001,China) |
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| Abstract: | Prepositional phrase which is constructed of a preposition and its reference is important in syntactic analysis.Tests choose the corpus of People's Daily based on word segmentation,tagging,and annotation of prepositional phrases.From which,61 frequently used prepositions are chosen;Statistical models such like SVM,ME and CRF are used to automatically identify the boundary of prepositional phrases.The results of test show that CRF outperform the other two models,it achieves a micro precision of 95.68%. |
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| Keywords: | preposition phrase SVM ME CRF |
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