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山东大学学报（理学版） ›› 2015, Vol. 50 ›› Issue (07): 9-16.doi: 10.6040/j.issn.1671-9352.3.2014.026

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基于多源知识的中文微博命名实体链接

昝红英1, 吴泳钢1, 贾玉祥1, 牛桂玲2   

  1. 1. 郑州大学信息工程学院, 河南 郑州 450001;
    2. 郑州大学外语学院, 河南 郑州 450001
  • 收稿日期:2015-03-03 出版日期:2015-07-20 发布日期:2015-07-31
  • 作者简介:昝红英(1966-),女,博士,教授,研究方向为自然语言处理.E-mail:iehyzan@zzu.edu.cn
  • 基金资助:
    国家自然科学基金资助项目(61402419,60970083,61272221);国家社会科学基金资助项目(14BYY096);国家高技术研究发展计划863计划项目(2012AA011101);河南省科技厅科技攻关计划资助项目(132102210407);河南省科技厅基础研究资助项目(142300410231,142300410308);河南省教育厅科学技术研究重点项目(12B520055,13B520381);计算语言学教育部重点实验室(北京大学)开放课题资助项目(201401)

Chinese Micro-blog named entity linking based on multisource knowledge

ZAN Hong-ying1, WU Yong-gang1, JIA Yu-xiang1, NIU Gui-ling2   

  1. 1. School of Information Engineering, Zhengzhou University, Zhengzhou 450001, Henan, China;
    2. School of Foreign Language, Zhengzhou University, Zhengzhou 450001, Henan, China
  • Received:2015-03-03 Online:2015-07-20 Published:2015-07-31

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摘要： 命名实体在文本中是承载信息的重要单元,而微博作为一种分享简短实时信息的社交网络平台,其文本长度短、不规范,而且常有新词出现,这就需要对其命名实体进行准确的理解,以提高对文本信息的正确分析。提出了基于多源知识的中文微博命名实体链接,把同义词词典、百科资源等知识与词袋模型相结合实现命名实体的链接。在NLP&CC2013中文微博实体链接评测数据集进行了实验,获得微平均准确率为92.97%,与NLP&CC2013中文实体链接评测最好的评测结果相比,提高了两个百分点。

关键词: 命名实体, 中文微博实体链接, 同义词词典, 百科资源, 词袋模型

Abstract: Named entity is an important component conveying information in texts. Micro-blog is a social network platform used to share brief real-time information, with characteristics such as short text length, nonstandard words, and even the frequent emergence of neologisms.So an accurate understanding of the named entities is needed to ensure a correct analysis of the text information. A Chinese Micro-blog entity linking strategy was proposed based on multi-resource knowledge, combing the dictionary of synonyms, the encyclopedia resources as well as the bag-of-words model together to deal with named entity linking.In this strategy, named entities to be linked in Micro-blog were mapped to the corresponding candidate entities in the knowledge base. The evaluation results obtain a micro average accuracy of 92.97%, based on experiments using data sets of NLP& CC2013 Chinese micro-blog entity linking track. Compared with the state-of-the-art result, the accuracy of this method is two percent higher,which demonstrates the effectiveness of our method.

Key words: named entity, dictionary of synonyms, bag-of-words model, encyclopedia resources, Chinese Micro-blog entity linking

中图分类号: 

  • TP391
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