论文标题
RDSGAN：基于等级的远处监督关系提取与生成对抗框架
RDSGAN: Rank-based Distant Supervision Relation Extraction with Generative Adversarial Framework
论文作者
论文摘要
遥远的监督已被广泛用于关系提取，但遇到了噪声标记问题。提出了神经网络模型，以通过注意机制来贬低，但由于其非零权重，无法消除嘈杂的数据。提出了艰难的决定，以从积极集合中删除错误标记的实例，尽管导致删除实例中包含的有用信息的丢失。在本文中，我们提出了一个名为RDSGAN（基于等级的遥远监督GAN）的新型生成神经框架，该框架自动生成有效的实例，以进行远处的监督关系提取。我们的框架结合了通过对抗训练来学习真正积极实例的分布，并选择通过基于等级的远处监督符合分配的有效实例，这将解决误报问题。实验结果表明，我们的框架优于强基础。
Distant supervision has been widely used for relation extraction but suffers from noise labeling problem. Neural network models are proposed to denoise with attention mechanism but cannot eliminate noisy data due to its non-zero weights. Hard decision is proposed to remove wrongly-labeled instances from the positive set though causes loss of useful information contained in removed instances. In this paper, we propose a novel generative neural framework named RDSGAN (Rank-based Distant Supervision GAN) which automatically generates valid instances for distant supervision relation extraction. Our framework combines soft attention and hard decision to learn the distribution of true positive instances via adversarial training and selects valid instances conforming to the distribution via rank-based distant supervision, which addresses the false positive problem. Experimental results show the superiority of our framework over strong baselines.
