Joint entity and relation extraction combined with multi-module feature information enhancement

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%.