## 2 Related Work  
Traditional methods for entity and relation extraction usually adopt a pipeline learning framework in which entity and relation extraction tasks are treated as two independent and sequential tasks. Although this framework is flexible, it has limitations such as the possibility of errors in named entity recognition affecting the accuracy of subsequent relation extraction tasks, and the neglect of the inherent connections between entities and relationships, leading to insufficient semantic expression in single-task modeling, which restricts model performance [9; 20]. Recently, joint extraction methods have been widely used, which use a unified model for learning and extracting entities and relationships. This approach can integrate the unique information of entities and relationships, enriching the features used in both sub-tasks, and training entity recognition and relationship extraction tasks simultaneously can facilitate optimization and improve the performance of both tasks [6]. There are three commonly used joint extraction models: sequence labeling models, pointer network models, and span-based models. Sequence labeling models are commonly used in named entity recognition and convert the joint extraction task into a sequence labeling prediction task, allowing for the extraction of entities and relations in a single model. [PERSON] et al. [10] used a relation-aware attention mechanism to construct specific sentence representations for each relation, and then performed sequence labeling to extract their corresponding head and tail entities. [PERSON] et al. [5] proposed a new labeling scheme and an end-to-end model with a biased objective function to jointly extract entities and their relations. However, this model cannot handle the problem of entity overlap. The pointer network is a more complex sequence labeling model that uses multiple annotation sequences to represent entities in a sentence and classifies the relationships between entities, thereby solving the problem of entity-relation overlap. [PERSON] et al. [21] proposed a relation extraction model based on a double pointer network with multi-head attention mechanism, using the forward decoder of an object decoder to find n-to-1 subject-object relationships and the backward decoder of a subject decoder to find 1-to-n subject-object relationships. [PERSON] et al. [11] adopted an encoder-decoder architecture with a pointer network-based decoding framework, which generates a complete opinion triplet at each time step. The decoder captures the interaction between aspects and opinions by considering their entire detection span while predicting their connection sentiment. On the other hand, [PERSON] et al. [12] proposed a novel BERT-based enhanced lexical adapter (BLA) model, which deeply integrates external dictionary features into the pre-trained language model BERT. However, the pointer network is vulnerable to imbalanced labels, which affects its performance, and its decoding process is complicated, requiring a special program to determine entities based on the pointer network results. Span-based models identify all possible word spans in text and construct a classification module to predict whether a span is an entity and whether there is a relationship between entities. This type of model can directly obtain entities and relations, solving the problem of entity-relation overlap, and does not require a complex transformation process from labels to entities, making it currently the more advantageous method for joint entity-relation extraction. Several span-based models have been proposed, such as DyGIE [6], which uses a dynamically constructed span graph to share span representations, propagate coreference relationships and relation types in the dynamic graph, and facilitate iterative updates of the span representations.