Unsupervised Relation Extraction (RE) aims to identify relations between entities in text, without having access to labeled data during training. This setting is particularly relevant for domain specific RE where no annotated dataset is available and for open-domain RE where the types of relations are a priori unknown. Although recent approaches achieve promising results, they heavily depend on hyperparameters whose tuning would most often require labeled data. To mitigate the reliance on hyperparameters, we propose PromptORE, a ''Prompt-based Open Relation Extraction'' model. We adapt the novel prompt-tuning paradigm to work in an unsupervised setting, and use it to embed sentences expressing a relation. We then cluster these embeddings to discover candidate relations, and we experiment different strategies to automatically estimate an adequate number of clusters. To the best of our knowledge, PromptORE is the first unsupervised RE model that does not need hyperparameter tuning. Results on three general and specific domain datasets show that PromptORE consistently outperforms state-of-the-art models with a relative gain of more than 40% in B 3 , V-measure and ARI. Qualitative analysis also indicates PromptORE's ability to identify semantically coherent clusters that are very close to true relations.

翻译：无监督关系抽取旨在从文本中识别实体间关系，且训练过程中无需访问标注数据。该设置尤其适用于以下两类场景：一是缺乏标注数据集的特定领域关系抽取，二是关系类型先验未知的开放领域关系抽取。尽管近期方法取得了令人瞩目的成果，但它们高度依赖超参数，而这些超参数的调优通常需要标注数据。为缓解对超参数的依赖，我们提出PromptORE——一种"基于提示的无监督关系抽取"模型。我们将新型提示调优范式适配至无监督场景，并利用其编码表达关系的句子。随后通过聚类这些嵌入向量发现候选关系，并实验多种自动估算合理聚类数目的策略。据我们所知，PromptORE是首个无需超参数调优的无监督关系抽取模型。在三个通用及特定领域数据集上的实验表明，PromptORE在B³、V-measure和ARI指标上以超过40%的相对增益持续超越现有最优模型。定性分析同时证实，PromptORE能够识别语义一致且高度逼近真实关系的聚类结构。