## 2 Related Work  
The extraction of entities and relations based on deep learning is mainly divided into pipeline and joint extraction methods [9]. Previous pipeline methods primarily used two major classes of structures to separate entity and relation extraction: Convolutional Neural Networks (CNNs) [10] and Recurrent Neural Networks (RNNs) [11]. Subsequent research based on CNNs and RNNs led to many variants such as Long Short-Term Memory (LSTM) [12] and Bidirectional Long Short-Term Memory (Bi-LSTM) [13]. [PERSON] et al. developed an SDP-LSTM relation extraction model based on the shortest path of the syntactic dependency parsing tree and improved its accuracy by incorporating word vector features, part-of-speech features, WordNet features, and syntactic type features [14]. [PERSON] et al. utilized the Bi-LSTM model to combine information before and after the current word for relation extraction [15]. [PERSON] et al. introduced an attention mechanism to Bi-LSTM for capturing both local and global features of input sentences [16]. Although the pipeline method has achieved more success in entity relation extraction, its splitting approach often overlooks the connections between entity relations, which leads to issues such as interaction omissions, error propagation, and entity redundancy. The joint extraction method involves extracting entities and relations simultaneously, which can leverage the interaction information between entities and relations to enhance the model's performance. [PERSON] et al. developed an end-to-end entity relation extraction method to share parameters between entity recognition and relation extraction tasks to strengthen the correlation between such two tasks [17]. The above models only shared the bidirectional sequence LSTM representation at the encoding layer and only treated named entity recognition and relation extraction as two separate tasks rather than performing true joint extraction. However, the introduction of these models still laid the foundation for subsequent joint extraction methods. [PERSON] et al. proposed a joint tagging strategy called NovelTagging, which achieved joint decoding. They transformed the entity relation extraction problem into a sequence labeling problem, enabling the simultaneous identification of entities and relations [18]. Focused on the problems of overlapping triples and nested entities, several models have been proposed to address these two issues. For example, [PERSON] et al. proposed a new strategy called ETL-Span, which decomposed the joint extraction task into two tasks based on Span's labeling scheme. The two tasks applied are divided into head entity extraction and tail entity relation extraction [19]. Based on ETL-Span, [PERSON] et al. proposed a cascaded binary annotation framework known as CasRel, which first identified all possible subjects in a sentence by using a start position classifier and an end position classifier. Further, for each subject, span-based labeling is applied to identify the corresponding objects with a classifier based on each relation [20]. Several researchers have also improved relative methods by introducing sentence encoding with the general framework of this model, with examples including IDCNN (Iterated Dilated CNN) and Bi-LSTM [21]. The CB-Chinese-Bert integrated self-attention mechanism for Chinese text encoding was presented, and it can be effective in obtaining richer semantic features in sentences [22]. In addition, though Casrel addresses the overlap issues, its framework still suffers from low computational efficiency and numerous requirements for evaluating redundant relationships. Moreover, the framework identification of nested entities requires more accurate results. [PERSON] et al. proposed a model called TPLinker designed to extract information in a single stage. The model unifies the extraction tagging framework into a character pair linking problem, which allows for multi-head tagging through matrices and aligns the subject with the object under specific relations [23].