## 2 Related Works
Building upon this research, [PERSON] et al. [29] proposed an HBT framework, where they regarded the relation extraction task as a multi-turn question-answering task, incorporating external knowledge to introduce entities and relations. However, generating appropriate questions remains a challenge and is not suitable for most complex scenarios [30]. Inspired by the work of [PERSON] et al. [26] and [PERSON] et al. [28], [PERSON] et al. [22] introduced graphical convolutional networks, which are widely used in the field of logical reasoning [31], into the task of relation extraction. After identifying the relation between entity pairs, graph convolutional networks are used to infer the possibility of the existence of other relations. For example, \"{[PERSON], LiveIn, United States}\" can be inferred from \"{[PERSON], LiveIn, Florida}\". Prior to this, [PERSON] et al. [4] proposed a strong neural end-to-end joint model based on an LSTM sequence tagger for entities and relations, which helped infer unidentified relations based on identified relations. However, they were unable to address the relation overlapping problem. Although current research has achieved joint extraction of entities and relations and developed models to address the relation overlapping problem, relations are still treated as discrete labels for entity pairs. This makes it difficult for models to correctly extract overlapping triples. [PERSON] et al. [32] proposed a new cascaded binary labeling framework (CasRel), which uses BERT [33] as the feature extraction method and maps the tail entities through the head entities conditioned on the relations. As shown in the equation, \(r\) stands for relation, \(S\) stands for head entity, and \(O\) stands for tail entity. Relations are modeled as functions that map the head entity to the tail entity:  
\[F_{r}(S)\to O \tag{1}\]  
In natural language, the relations between entities are often closely related to their context. By introducing information about the head entity, the model can better understand the semantic structure of the sentence, thus predicting the tail entity and relations more accurately. Additionally, entities in a sentence may have multiple roles or meanings, but when paired with a specific head entity, their roles or meanings become clearer, reducing ambiguity in the tail entity and relations and improving the accuracy of identifying them. Therefore, CasRel has achieved good results. However, [PERSON] et al. [32] were still unable to effectively utilize head entity information. Specifically, they only used head entity information as a parametric feature when identifying tail entities and relations, ignoring the potential role of head entity information in reducing word noise. When the problem of overlapping relations occurs, especially in cases of multiple overlapping relations, numerous entities typically emerge in the text. However, most of these entities do not participate in forming effective relations. This situation leads to the generation of a large number of potential invalid entity pairs, making it difficult for the model to accurately identify truly effective entity pairs among numerous possibilities, ultimately affecting the overall accuracy of relation extraction tasks. In order to utilize the head entity information to reduce text noise and minimize interference from other irrelevant entities, we propose the Relation extraction method based on the Entity Attention network and Cascade binary Tagging framework (REACT). We utilize Entity Attention networks to help the model focus on words that are highly relevant to the head entity and the relation extraction task, thereby reducing the likelihood of identifying errors in tail entities and relations and improving the accuracy of extracting entity relation triplets.