## 1 Introduction  
Entity and relation extraction (ERE) is the process of automatically identifying entities and the relations between them from natural language text. This task includes two subtasks: named entity recognition (NER) and relation extraction (RE). ERE plays a crucial role in various applications [1]. In practical applications, entity-relation extraction techniques have been widely used in knowledge graphs, question-and-answer systems, information retrieval, intelligent customer service, and other fields. Traditional supervised entity-relation extraction methods are mainly divided into feature engineering-based methods and kernel function based methods [2; 3]. However, these methods rely on manual feature extraction, which can lead to issues such as error propagation and limited effectiveness in capturing long-tailed entities and complex relations. In recent years, there has been a surge of research on entity-relation extraction methods based on deep learning. These methods have demonstrated consistent improvements in performance across multiple datasets [4; 5; 6; 7]. Two commonly used categories of deep learning-based supervised methods for entity-relation extraction are pipelined learning and joint learning. In pipelined learning, the entity extraction task is performed first using approaches based on recurrent neural networks(RNNs), convolutional neural networks (CNNs), or long short-term memory (LSTM). Subsequently, the relationship extraction task is carried out. For instance, MV-RNN [8] was the first to propose using RNN for entity-relation extraction, building semantic vector representations of words and phrases based on parse trees. However, this approach relies on the syntax tree used in the process, and errors in syntactic analysis can affect the final results of the model. [PERSON] et al. [9] proposed a method for entity-relation extraction based on LSTM by combining the shortest path of dependency analysis with word vector features and lexical features. However, all of these methods encounter the issue of error propagation, wherein an error in entity recognition can significantly impact the subsequent relation extraction. Furthermore, the interdependence between the two subtasks of entity extraction and relation extraction is frequently overlooked. In contrast, the joint ERE model offers a specific methodological framework for addressing ERE tasks. This model leverages shared features and inherent connections between the subtasks of entity extraction and relation extraction. By doing so, it efficiently and effectively extracts entities and relations from textual data, resulting in improved performance [4]. This approach can also avoid problems with error propagation. Deep learning models based on sequence labeling frameworks are a common method for joint extraction of entity and relations. However, they are unable to identify overlapping entities, which are frequently found in natural language [5; 10]. To overcome this limitation, pointer network-based approaches have been proposed, which use multiple pointer networks for multi-sentence annotation and multiple tags to represent a sentence [11; 12]. However, such an approach may suffer from label imbalance. Therefore, more recent methods are attempting to use novel span-based entity-relation joint extraction models to handle ERE tasks [13; 14; 15]. In the span-based joint entity-relation extraction method, word spans in a sentence are enumerated, and the representation embedding of spans is shared in one model to accomplish the tasks of NER and RE. This method overcomes issues such as overlapping entity recognition and repeated encoding in entity-relation joint extraction models [13; 14; 15]. For this method, named entity recognition is regarded as a classification task for each word span sample, and relation extraction is treated as a relation classification task for the combinations of spans and context embeddings.