基于注意力机制语义增强的文档级关系抽取

文档级关系抽取旨在从文档中抽取出多个实体对之间的关系，具有较高的复杂性。针对文档级关系抽取中的多实体、关系相关性、关系分布不平衡等问题，提出了一种基于注意力机制（Attention）语义增强的文档级关系抽取方法，能够实现实体对之间关系的推理。具体来说，首先在数据编码模块改进编码策略，引入更多实体信息，通过编码网络捕获文档的语义特征，获得实体对矩阵；然后，设计了一个基于Attention门控机制的U-Net网络，对实体对矩阵进行局部信息捕获和全局信息汇总，实现语义增强；最后，使用自适应焦点损失函数缓解关系分布不平衡的问题。在4个公开的文档级关系抽取数据集（DocRED、CDR、GDA和DWIE）上评估了Att-DocuNet模型并取得了良好的实验结果。

Document-Level Relation Extraction Method Based on Attention Semantic Enhancement

Document-level relation extraction aims to extract the relations between multiple entity pairs from a document， a task characterized by high complexity. This paper proposes a method for document-level relation extraction based on attention semantic enhancement to address challenges such as handling multiple entities， capturing relationship correlations， and dealing with imbalanced relationship distributions within documents. The method proposed facilitates the inference of relationships between entity pairs. Specifically， the data encoding module enhances the encoding strategy by incorporating additional entity information， capturing semantic features of the document through the encoding network， and generating an entity pair matrix. Subsequently， a U-Net network employing an attention gating mechanism is devised to capture local information and aggregate global information from entity pair matrices， thereby achieving semantic enhancement. Finally， this paper introduces an adaptive focal loss function to mitigate imbalanced relationship distributions. The Att-DocuNet model proposed is evaluated on four publicly available document-level relation extraction datasets （DocRED， CDR， GDA， and DWIE）， yielding promising experimental results.