Joint entity and relation extraction is the fundamental task of information extraction, consisting of two subtasks: named entity recognition and relation extraction. Most existing joint extraction methods suffer from issues of feature confusion or inadequate interaction between two subtasks. In this work, we propose a Co-Attention network for joint entity and Relation Extraction (CARE). Our approach involves learning separate representations for each subtask, aiming to avoid feature overlap. At the core of our approach is the co-attention module that captures two-way interaction between two subtasks, allowing the model to leverage entity information for relation prediction and vice versa, thus promoting mutual enhancement. Extensive experiments on three joint entity-relation extraction benchmark datasets (NYT, WebNLG and SciERC) show that our proposed model achieves superior performance, surpassing existing baseline models.

翻译：联合实体与关系抽取是信息抽取中的基础任务，包含两个子任务：命名实体识别和关系抽取。现有多数联合抽取方法存在特征混淆或两个子任务间交互不足的问题。本文提出一种面向联合实体关系抽取的协同注意力网络（CARE）。该方法为每个子任务学习独立表示，旨在避免特征重叠。其核心是协同注意力模块，该模块能捕捉两个子任务间的双向交互，使模型既能利用实体信息预测关系，也能利用关系信息辅助实体识别，从而实现相互增强。在三个联合实体关系抽取基准数据集（NYT、WebNLG和SciERC）上的大量实验表明，本文提出的模型取得了优越性能，超越了现有基线模型。