电子学报2018，Vol.46Issue(3)：607-613,7.DOI:10.3969/j.issn.0372-2112.2018.03.014
基于双词主题模型的半监督实体消歧方法研究
Semi-supervised Entity Disambiguation Method Research Based on Biterm Topic Model
摘要
Abstract
Aimed at the problem of theme drift of the entity context information, this paper proposes an entity disambiguation method based on biterm topic model. The proposed method considers that the entity has a different theme in a certain semantic environment and the other entity appearing in the same document at the same time can help the disambiguated entity to determine the referred content to a certain extent. Therefore, using the ideas of named entity constructing double words to incorporate collaborative entity relationship to the topic model, and on this basis, we conduct semi-supervised disambiguation using Wikipedia knowledge base. Finally, this paper conducts some relevant experiments on the web text data, and verifies the effectiveness of the proposed algorithm. The experiments show that the proposed method effectively improve the precision of entity disambiguation.关键词
实体消歧/维基百科/双词主题模型Key words
entity disambiguation/Wikipedia/biterm topic model分类
信息技术与安全科学引用本文复制引用
张雄,陈福才,黄瑞阳..基于双词主题模型的半监督实体消歧方法研究[J].电子学报,2018,46(3):607-613,7.基金项目
国家自然科学基金(No.61171108) （No.61171108）
国家重点基础研究发展计划("973"计划)资金(No.2012CB315901, No.2012CB315905 ) （"973"计划）
国家科技支撑计划(No.2014BAH30B01) （No.2014BAH30B01）
