  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.
