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浙江大学学报(工学版)
JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE)
    
Cross-document personal name disambiguation merging sentential semantic analysis
ZHANG Han, LUO Sen-lin, ZOU Li-li, SHI Xiu-min
School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China
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Abstract  

A multi-stage disambiguation algorithm was proposed based on the construction of text feature space. According to the characteristics of query terms often occurring as common terms, heuristic rule was applied to determine if the query term is personal name after the pre-processing of documents. Then named entity and occupation were extracted according to the feature templates. The sentential semantic model was used for sentential semantic analysis and sentential semantic features extraction. The word frequency was counted according to the bag-of-words model. Then the three layers of feature space were constructed. The rule-based classification and two-stage hierarchical clustering algorithm was used to realize the name disambiguation. The overlap coefficient was introduced to compute the similarity of the sentential semantic features. The experiments datasets built by CLP2012 Chinese Personal Name disambiguation showed that F achieved 88.79%, which proved that the proposed approach can improve the performance of cross-document personal name disambiguation.

Published: 01 April 2015
CLC:  TP 391  
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Cite this article:

ZHANG Han, LUO Sen-lin, ZOU Li-li, SHI Xiu-min. Cross-document personal name disambiguation merging sentential semantic analysis. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2015, 49(4): 717-723.

URL:

http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2015.04.016     OR     http://www.zjujournals.com/eng/Y2015/V49/I4/717


融合句义分析的跨文本人名消歧

在构造文本特征空间的基础上,提出融合句义分析的三阶段人名消歧方法.该方法针对查询词常作为普通词出现的特点,在文本预处理后采用启发式规则的后处理方法判断查询词是否指人名,根据特征模板提取局部名实体特征及职业.通过句义结构模型进行句义分析,提取句义特征,利用词袋模型统计词频,构成三层特征空间,使用基于规则的分类和两阶段层次聚类算法实现人名消歧.引入重叠系数计算句义特征相似度,在CLP2012中文人名消歧语料上进行实验,F达到88.79%,证明了将句义分析应用到跨文本人名消歧的效果良好.

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