Distantly supervised relation extraction with a Meta-Relation enhanced Contrastive learning framework

Distantly supervised relation extraction employs the alignment of unstructured corpora with knowledge bases to automatically generate labeled data. This method, however, often introduces significant label noise. To address this, multi-instance learning has been widely utilized over the past decade, aiming to extract reliable features from a bag of sentences. Yet, multi-instance learning struggles to effectively distinguish between clean and noisy instances within a bag, thereby hindering the full utilization of informative instances and the reduction of the impact of incorrectly labeled instances. In this paper, we propose a new Meta-Relation enhanced Contrastive learning based method for distantly supervised Relation Extraction named MRConRE. Specifically, we generate a “meta relation pattern” ($\text{MRP}$) for each bag, based on its semantic content, to differentiate between clean and noisy instances. Noisy instances are then transformed into beneficial bag-level instances through relabeling. Subsequently, contrastive learning is employed to develop precise sentence representations, forming the overall representation of the bag. Finally, we utilize a mixup strategy to integrate bag-level information for model training. Our method’s effectiveness is validated through experiments on various benchmarks.