Exploring Long Tail Data in Distantly Supervised Relation Extraction

被引:6
|
作者
Gui, Yaocheng [1 ,2 ]
Liu, Qian [3 ]
Zhu, Man [3 ]
Gao, Zhiqiang [1 ,2 ]
机构
[1] Southeast Univ, Key Lab Comp Network & Informat Integrat, Minist Educ, Nanjing, Jiangsu, Peoples R China
[2] Southeast Univ, Sch Comp Sci & Engn, Nanjing, Jiangsu, Peoples R China
[3] Nanjing Univ Posts & Telecommun, Sch Comp Sci & Technol, Nanjing, Jiangsu, Peoples R China
基金
美国国家科学基金会;
关键词
Distant supervision; Explanation-based learning; Relation extraction;
D O I
10.1007/978-3-319-50496-4_44
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
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.
引用
收藏
页码:514 / 522
页数:9
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