Named Entity Recognition and Relation Extraction are two crucial and challenging subtasks in the field of Information Extraction. Despite the successes achieved by the traditional approaches, fundamental research questions remain open. First, most recent studies use parameter sharing for a single subtask or shared features for both two subtasks, ignoring their semantic differences. Second, information interaction mainly focuses on the two subtasks, leaving the fine-grained informtion interaction among the subtask-specific features of encoding subjects, relations, and objects unexplored. Motivated by the aforementioned limitations, we propose a novel model to jointly extract entities and relations. The main novelties are as follows: (1) We propose to decouple the feature encoding process into three parts, namely encoding subjects, encoding objects, and encoding relations. Thanks to this, we are able to use fine-grained subtask-specific features. (2) We propose novel inter-aggregation and intra-aggregation strategies to enhance the information interaction and construct individual fine-grained subtask-specific features, respectively. The experimental results demonstrate that our model outperforms several previous state-of-the-art models. Extensive additional experiments further confirm the effectiveness of our model.

翻译：命名实体识别和关系抽取是信息抽取领域中两个关键且具有挑战性的子任务。尽管传统方法取得了一定成功，但基础性问题仍有待探索。首先，大多数近期研究采用针对单一子任务的参数共享或针对两个子任务的共享特征，忽略了它们在语义上的差异。其次，信息交互主要聚焦于这两个子任务，而未充分探索编码主体、关系和客体时子任务特定特征之间的细粒度信息交互。基于上述局限，我们提出一种新颖的模型来联合抽取实体与关系。主要创新如下：（1）提出将特征编码过程解耦为三部分，即编码主体、编码客体和编码关系。由此，我们能够利用细粒度的子任务特定特征。（2）提出新颖的间聚合和内部聚合策略，分别用于增强信息交互和构建独立的细粒度子任务特定特征。实验结果表明，我们的模型优于若干先前的最先进模型。广泛的附加实验进一步验证了模型的有效性。