Document-level multiple relations extraction method via evidence guidance and relation correlation

Abstract

Document-level Relation Extraction (DocRE) aims to extract semantic relations between entity pairs, spanning multiple sentences, paragraphs or even the entire document. These relations can often be predicted by partial sentences within the document, the evidence sentence. However, the relation derived only from sentence information is incomplete, because it ignores the case of multiple relations between entity pairs. Therefore, how to select effective evidence sentences and how to predict multiple relations more accurately have become challenges for the existing DocRE models. In response to these challenges, we introduce Reinforcement Learning (RL) to select more effective evidence sentences, while using heuristic rules to narrow down the search space of RL. Secondly, we utilize GAT to acquire the features of co-occurrence relations, which can greatly improve multiple relations prediction performance. Moreover, the combination of the features of co-occurrence relations and the evidence sentence information enables our method to achieve both high effectiveness and precision. The experimental results show that, compared with other advanced methods, our method achieves an F1 score of 66.56 and the EviF1 score of 56.69, which attains the state-of-the-art performance on public datasets.