关键词: 文档级关系抽取, 关系回指图, 逻辑规则, 样本不平衡, 加权长尾损失函数

Abstract: In the task of document-level relation extraction, existing models mainly focus on learning the interaction between entities, neglecting the learning of entity internal structures, and paying little attention to the identification of pronoun references and the utilization of logical rules in the document. It leads to the model not being accurate enough in modeling relationships between entities in the document. To address this issue, an anaphor-aware graph is integrated into the Transformer architecture to model the interaction among entities and their internal structures. By leveraging anaphors, more contextual information is aggregated onto corresponding entities to enhance the accuracy of relation extraction. Moreover, a data-driven approach is used to mine logical rules from relation annotations to enhance the understanding and reasoning capability of implicit logical relationships in the text. To solve the problem of sample imbalance, a weighted long-tail loss function is introduced to improve the accuracy of identifying rare relations. The model was validated on two public datasets DocRED and Re-DocRED. The experimental results show that performance of the proposed model is better than those of the comparison models.

Key words: Keywords: document-level relation extraction, anaphor-aware graph, logical rules, sample imbalance, weighted long tail loss function