提出新框架，让模型同时识别已知和未知关系。
Towards a More Generalized Approach in Open Relation Extraction
- 分两阶段联合学习已知与未知关系，适应真实数据混合分布。
- 在三个基准数据集上，已知关系识别与未知关系聚类均优于基线。
- 适合需要处理复杂真实场景的开放关系抽取任务。
开放关系抽取（OpenRE）旨在从无标注数据中识别并提取命名实体间的新型关系，而无需预定义的关系模式。传统方法通常假设无标注数据仅包含新型关系，或已预先划分为已知与新型实例。然而，在真实场景中，新型关系是任意分布的。本文提出一种更通用的OpenRE设定，将无标注数据视为已知与新型实例的混合。为此，我们提出MixORE，一个两阶段框架，通过联合进行关系分类与聚类，共同学习已知与新型关系。在三个基准数据集上的实验表明，MixORE在已知关系分类和新型关系聚类任务中均持续优于竞争性基线。研究结果推动了通用OpenRE的发展及其实际应用。
原文摘要 · Abstract (English)
Open Relation Extraction (OpenRE) seeks to identify and extract novel relational facts between named entities from unlabeled data without pre-defined relation schemas. Traditional OpenRE methods typically assume that the unlabeled data consists solely of novel relations or is pre-divided into known and novel instances. However, in real-world scenarios, novel relations are arbitrarily distributed. In this paper, we propose a generalized OpenRE setting that considers unlabeled data as a mixture of both known and novel instances. To address this, we propose MixORE, a two-phase framework that integrates relation classification and clustering to jointly learn known and novel relations. Experiments on three benchmark datasets demonstrate that MixORE consistently outperforms competitive baselines in known relation classification and novel relation clustering. Our findings contribute to the advancement of generalized OpenRE research and real-world applications.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。