## 2 Related Works  
Early works on relation extraction adopted a pipeline approach ([PERSON] et al. (2018); [PERSON] et al. (2012); [PERSON] et al. (2013); [PERSON] et al. (2014); and [PERSON] et al. (2015)). It first identifies all the entities in a sentence and then performs relation classification for each entity pair. This approach often faces the problem of error propagation because errors in the early stages cannot be corrected in later stages. To address this problem, subsequent works proposed joint learning of entities and relations, which includes feature-based models ([PERSON] et al. (2016); [PERSON] et al. (2017); and [PERSON] et al. (2018)), as well as more recent neural network models ([PERSON] et al. (2019); [PERSON] et al. (2020); [PERSON] et al. (2021); and [PERSON] et al. (2022)). By replacing manually constructed features with learned representations, neural network-based models have achieved considerable success in the relation extraction task. As research has progressed, the relation overlapping problem has been increasingly emphasized. The relation overlapping problem refers to the situation where there are shared entities among the entity-relation triad in the text. To address this problem, many neural network-based joint models have been proposed ([PERSON] et al. (2019)). [PERSON] et al. (2019) were among the first to consider the relation overlapping problem. They first divided the relation overlapping problem into Normal, SEO, and EPO, and proposed a sequence-to-sequence (Seq2 Seq) model with a copying mechanism. To address the relation overlapping problem, they allowed entities to participate freely in multiple triples. Building upon the Seq2 Seq model, [PERSON] et al. (2019) further investigated the impact of triple extraction order on relation extraction performance, transforming the relation extraction task into a reinforcement learning process, resulting in significant improvements. [PERSON] et al. [26] redesigned the relation extraction method and successfully enabled the model to handle the relation overlapping problem. They first identified all entity pairs in the text and then individually determined whether each relation existed, rather than extracting only the most probable relation. Additionally, they pointed out that previous relation extraction methods ignored the connections between relations and independently predicted each possible relation. For example, if an entity pair has the \"liveIn\" relation, the \"dieln\" relation is almost impossible to establish. Therefore, when determining the existence of a certain relation between entities, it is necessary to not only consider the target relation but also calculate the probability scores with respect to other relations. Inspired by the aforementioned research, [PERSON] et al. [27] assumed that the importance of words in text varies across different relations. They constructed a joint model based on a specific attention mechanism, which first identifies the relation existing in the text, then incorporates the relation into the attention mechanism, and finally identifies entity pairs containing the relation based on attention scores. Although the above research was able to address the relation overlapping problem, they still regarded the identification of head and tail entities as independent processes, ignoring the semantic and logical connections between them. For example, organizational entities usually have relations with human entities (leaders, founders, members, etc. ), but do not always have relations with entities such as songs, animals, movies, etc. [PERSON] et al. [28] re-designed the method to first identify the head entity, then identify the potential tail entities based on the head entity, and finally determine all possible relations that may exist between the head entity and the tail entities. This approach not only addressed the relation overlapping problem but also achieved outstanding performance by utilizing head entity information in the tail entity identification process.