Clustering-based Unsupervised Generative Relation Extraction

Existing unsupervised relation extraction methods work by extracting sentence features and using these features as inputs to train a generative model. This model is then used to cluster similar relations. However, these methods do not consider correlations between sentences with the same entity pair during training, which can negatively impact model performance. To address this issue, we propose a Clustering-based Unsupervised generative Relation Extraction (CURE) framework that leverages an Encoder-Decoder architecture to train a relation extractor as the encoder. Given multiple sentences with the same entity pair as inputs, CURE is deployed by predicting the shortest path between entity pairs on the dependency graph of one of the sentences. After that, we extract the relation information using the encoder. Then, entity pairs that share the same relation are clustered based on their corresponding relation information. Each cluster is labeled based on the words in the shortest paths corresponding to the entity pairs in each cluster. Experimental results demonstrate the effectiveness of CURE compared to state-of-the-art models across all benchmark datasets.