Unsupervised Joint Entity Linking over Question Answering Pair with Global Knowledge

摘要：

　　We consider the task of entity linking over question answering pair(QA-pair).In conventional approaches of entity linking,all the entities whether in one sentence or not are considered the same.We focus on entity linking over QA-pair,in which question entity and answer entity are no longer fully equiva-lent and they are with the explicit semantic relation.We propose an unsupervised method which utilizes global knowledge of QA-pair in the knowledge base(KB).Firstly,we collect large-scale Chinese QA-pairs and their corresponding triples in the knowledge base.Then mining global knowledge such as the probability of relation and linking similarity between question entity and answer entity.Finally integrating global knowledge and other basic features as well as constraints by integral linear programming(ILP)with an unsupervised method.The experimen-tal results show that each proposed global knowledge improves performance.Our best F-measure on QA-pairs is 53.7%,significantly increased 6.5%comparing with the competitive baseline.

关键词： joint entity linking question answering pair global knowledge in-tegral linear programming

作者: Cao Liu Shizhu He Hang Yang Kang Liu Jun Zhao

作者单位: National Laboratory of Pattern RecognitionInstitute of Automation,Chinese Academy of Sciences,Beijin National Laboratory of Pattern RecognitionInstitute of Automation,Chinese Academy of Sciences,Beijin

会议类型: 国内会议

会议名称: 第十六届全国计算语言学学术会议暨第五届基于自然标注大数据的自然语言处理国际学术研讨会

会议地点: 南京

会议语种:英文

页码: 1-12

在线出版日期: 2017-10-13（万方平台首次上网日期，不代表论文的发表时间）