摘要
人物重名现象十分普遍,搜索引擎的人名检索结果通常是多个同名人物相关网页的混合.该文依据同名的不同人物具有不同的社会网络的思想,利用检索结果中共现的人名发现并拓展检索人物相关的潜在社会网络,结合图的谱分割算法和模块度指标进行社会网络的自动聚类,在此基础上实现人名检索结果的重名消解.在人工标注的中文人名语料上进行实验,整体性能达到较好水平,图聚类算法能帮助连通社会网络的进一步划分,从而提高消解效果.
The person names are so ambiguous that the results for searching a person name are usually a mixture of pages about the namesakes. This paper presents a novel approach leveraging the fact that each namesake has a unique social community. Firstly, the social network of the person name to search is found and extended by employing the co-occurrence of person names in snippets returned by a search engine, then automatically clustered into different social communities by the algorithm combining spectral partition and modularity evaluation. Finally, the search results are clustered into different groups where each contains pages referring to the same individual. On the corpus of Chinese person names, experimental results show that the whole performance achieves high level and graph clustering algorithm benefits improving disambiguation effect from further dividing the connecting social network.
出处
《计算机学报》
EI
CSCD
北大核心
2009年第7期1365-1374,共10页
Chinese Journal of Computers
基金
国家自然科学基金(60675034
60803093)
国家"八六三"高技术研究发展计划探索类专题项目(2008AA01Z144)资助
关键词
社会网络
重名消解
谱分割
模块度
social network
name disambiguation
spectral partition
modularity
