Distantly supervision automatically generates plenty of training samples for
relation extraction. However, it also incurs two major problems: noisy labels
and imbalanced training data. Previous works focus more on reducing wrongly
labeled relations (false positives) while few explore the missing relations
that are caused by incompleteness of knowledge base (false negatives).
Furthermore, the quantity of negative labels overwhelmingly surpasses the
positive ones in previous problem formulations. In this paper, we first provide
a thorough analysis of the above challenges caused by negative data. Next, we
formulate the problem of relation extraction into as a positive unlabeled
learning task to alleviate false negative problem. Thirdly, we propose a
pipeline approach, dubbed \textsc{ReRe}, that performs sentence-level relation
detection then subject/object extraction to achieve sample-efficient training.
Experimental results show that the proposed method consistently outperforms
existing approaches and remains excellent performance even learned with a large
quantity of false positive samples.

本文提出了一种名为 ReRe 的管道方法，通过句子级别的关系检测和主谓对象提取来实现样本有效训练，将关系抽取问题转化为正无标记学习任务，缓解了负样本引起的误差问题，实验结果表明，该方法能够持续超越现有方法并且即使在学习大量的假阳性样本时仍保持良好的性能。