基于双转移损失的带噪声标签关系抽取实用框架
A Practical Framework for Relation Extraction with Noisy Labels Based on Doubly Transitional Loss
摘要：
无论是人工标注还是基于规则的自动标注都是关系抽取数据扩充的有效方法。然而，不可避免的错误标记问题，例如通过远程监督可能会恶化许多现有方法的性能。为了解决这个问题，我们引入了一个实用的端到端深度学习框架，包括一个标准的特征提取器和一个新的噪声分类器与我们提出的双重过渡机制。一个转换基本上是由隐藏层之间的非线性变换参数化的，隐式地表示真实标签和噪声标签之间的转换，并且它可以与其他模型参数一起容易地优化。另一种是显式概率转移矩阵，它捕获标签之间的直接转换，但需要从EM算法中导出。我们在NYT数据集和SemEval 2018任务7上进行了实验。实证结果表明，与最先进的方法相比，性能相当或更好。
英文摘要：
Either human annotation or rule based automatic labeling is an effective method to augment data for relation extraction. However, the inevitable wrong labeling problem for example by distant supervision may deteriorate the performance of many existing methods. To address this issue, we introduce a practical end-to-end deep learning framework, including a standard feature extractor and a novel noisy classifier with our proposed doubly transitional mechanism. One transition is basically parameterized by a non-linear transformation between hidden layers that implicitly represents the conversion between the true and noisy labels, and it can be readily optimized together with other model parameters. Another is an explicit probability transition matrix that captures the direct conversion between labels but needs to be derived from an EM algorithm. We conduct experiments on the NYT dataset and SemEval 2018 Task 7. The empirical results show comparable or better performance over state-of-the-art methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
