## 2 Related Work  
Entity-relation extraction tasks can generally be divided into methods based on generative pre-trained transformer models and methods based on representation learning. The generative pre-trained transformer-based approach treats entity and relation extraction as a text generation task, directly generating structured triplets that include entities and their relationships. Although generative pre-trained transformer models (such as ChatGPT and ChatGLM) have enabled significant breakthroughs in natural language processing, when handling specific complex tasks, they often rely on users designing complex prompts, constraining users' ability to utilize large language models (LLMs) for task-specific applications [9]. Currently, representation learning-based methods remain the primary approach for completing complex information extraction tasks. These methods can be broadly divided into two categories: pipeline methods [10; 11; 12; 13] and joint extraction methods. Pipeline methods treat entity recognition and relation extraction as two independent subtasks, which can result in interaction loss and error accumulation. To address these issues, subsequent studies introduced joint extraction methods to enable the simultaneous extraction of entities and their relations. Joint extraction methods improve the issues of error accumulation and the lack of interaction between subtasks. [PERSON] et al. [14] achieved the joint extraction of entities and relations through sequence labeling decoding. However, this method is limited by its label assignment mechanism and cannot effectively handle overlapping relations. Subsequent scholars have conducted in-depth research on triplet overlap. [PERSON] et al. [15] proposed a copy mechanism-based model that processes the task in two steps: first, it determines the relation, and then it maps the corresponding head and tail entities based on the identified relation. This approach improves both extraction accuracy and efficiency. [PERSON] et al. [16] developed the CasRel model, which uses relation-based cascaded binary tagging. The model first identifies all potential head entities in a sentence, and then iterates through the relations to find the corresponding tail entities. In leveraging the interdependencies between entities and relationships, the extraction quality is improved. However, the model is over-reliant on the extraction results of the head entities. [PERSON] et al. [17] proposed a joint extraction model based on a relation-aware attention network. Using the attention mechanism, the model first identifies relations in a sentence, and then performs sequence labeling to match the corresponding head and tail entities according to the identified relations, effectively improving triplet recognition in complex scenarios. [PERSON] et al. [18] proposed a joint extraction model based on a hybrid of characters and words. The model uses the attention mechanism output to mark the head entities, and it then fuses the attention output with the head entity information to map the corresponding relations and tail entities. [PERSON] et al. [19] proposed a bidirectional extraction framework called BiRTE that not only matches tail entities based on head entities but also matches head entities in reverse based on tail entities. This approach allows entity pairs to be extracted in two complementary directions. Subsequently, all possible relationships are matched for each extracted entity pair to generate complete entity-relation triplets, improving the quality of triplet extraction. This approach achieves good recognition performance. Most of these methods complete the joint extraction task in multiple stages, where the entity recognition results and relation extraction results are interdependent. While these methods effectively handle nested entities and overlapping triplets, errors from earlier stages often propagate to subsequent stages, increasing the risk of exposure bias. To address exposure bias in multi-stage processes, subsequent research introduced single-stage methods.