The proposed method for joint entity and relation extraction integrates the tasks of entity extraction and relation classification by sharing the encoding layer. However, the method faces challenges due to incongruities in the contextual information captured by these subtasks, resulting in potential feature conflicts and adverse effects on model performance. To address this, we introduced a novel joint entity and relation extraction method that incorporates multi-module feature information enhancement (MFIE) (https://github.com/liyao345496280/Relation-extraction). We employ a relation awareness enhancement module for the entity extraction task, which directs the model’s focus towards extracting entities closely related to potential relations using a potential relation extraction module and an attention mechanism. For the relation extraction task, we implement an entity information enhancement module that uses entity extraction results to augment the original feature information through a gating mechanism, thereby enhancing relation classification performance. Experiments on the NYT and WebNLG datasets demonstrate that our method performs well. Compared to the state-of-the-art method, the F1 score on the NYT dataset improved by 0.7%.

所提出的联合实体和关系提取方法通过共享编码层来集成实体提取和关系分类的任务。然而，由于这些子任务捕获的上下文信息不一致，该方法面临挑战，导致潜在的特征冲突并对模型性能产生不利影响。为了解决这个问题，我们引入了一种新颖的联合实体和关系提取方法，该方法结合了多模块特征信息增强（MFIE）（https://github.com/liyao345496280/Relation-extraction）。我们为实体提取任务采用了关系感知增强模块，该模块将模型的重点放在使用潜在关系提取模块和注意机制来提取与潜在关系密切相关的实体上。对于关系提取任务，我们实现了一个实体信息增强模块，该模块使用实体提取结果通过门控机制来增强原始特征信息，从而增强关系分类性能。在 NYT 和 WebNLG 数据集上的实验表明我们的方法表现良好。与最先进的方法相比，NYT 数据集上的 F1 分数提高了 0.7%。