论文标题
主动关系发现：迈向一般和标签 - 意识开放关系提取
Active Relation Discovery: Towards General and Label-aware Open Relation Extraction
论文作者
论文摘要
开放关系提取（OpenRE）旨在发现开放型领域的新关系。以前的OpenRE方法主要遇到两个问题：（1）不足以区分已知和新关系的能力。当将常规测试设置扩展到更通用的设置时，测试数据也可能来自可见的类别时，现有方法的性能会大大下降。 （2）必须在实际应用之前执行辅助标签。现有方法不能为新的关系标记人类可读和有意义的类型，这是下游任务急需的。为了解决这些问题，我们提出了主动关系发现（ARD）框架，该框架利用关系异常检测来区分已知和新颖的关系，并涉及积极学习以标记新的关系。在三个现实世界数据集上进行的广泛实验表明，在常规和我们提出的一般开放设置上，ARD明显优于先前的最新方法。源代码和数据集将用于可重复性。
Open Relation Extraction (OpenRE) aims to discover novel relations from open domains. Previous OpenRE methods mainly suffer from two problems: (1) Insufficient capacity to discriminate between known and novel relations. When extending conventional test settings to a more general setting where test data might also come from seen classes, existing approaches have a significant performance decline. (2) Secondary labeling must be performed before practical application. Existing methods cannot label human-readable and meaningful types for novel relations, which is urgently required by the downstream tasks. To address these issues, we propose the Active Relation Discovery (ARD) framework, which utilizes relational outlier detection for discriminating known and novel relations and involves active learning for labeling novel relations. Extensive experiments on three real-world datasets show that ARD significantly outperforms previous state-of-the-art methods on both conventional and our proposed general OpenRE settings. The source code and datasets will be available for reproducibility.
