Exploring Long Tail Data in Distantly Supervised Relation Extraction

摘要：

　　Distant supervision is an efficient approach for various tasks,such as relation extraction.Most of the recent literature on distantly supervised relation extraction generates labeled data by heuristically aligning knowledge bases with text corpora and then trains supervised relation classification models based on statistical learning.However,extracting long tail relations from the automatically labeled data is still a challenging problem even in big data.Inspired by explanation-based learning(EBL),this paper proposes an EBL-based approach to tackle this problem.The proposed approach can learn relation extraction rules effectively using unlabeled data.Experiments on the New York Times corpus demonstrate that our approach outperforms the baseline approach especially on long tail data.

关键词： Distant Supervision Explanation-Based Learning Relation Extraction

作者: Yaocheng Gui Qian Liu Man Zhu Zhiqiang Gao

作者单位: Key Lab of Computer Network and Information Integration(Southeast University),Ministry of Education, School of Computer Science and Technology,Nanjing University of Posts and Telecommunications,China

会议类型: 国际会议

会议名称: 第五届自然语言处理与中文计算会议(NLPCC-ICCPOL2016)

会议地点: 昆明

会议语种:英文

页码: 1-8

在线出版日期: 2016-12-02（万方平台首次上网日期，不代表论文的发表时间）