###### Abstract  
With the development of the Internet, vast amounts of text information are being generated constantly. Methods for extracting the valuable parts from this information have become an important research field. Relation extraction aims to identify entities and the relations between them from text, helping computers better understand textual information. Currently, the field of relation extraction faces various challenges, particularly in addressing the relation overlapping problem. The main difficulties are as follows: (1) Traditional methods of relation extraction have limitations and lack the ability to handle the relation overlapping problem, requiring a redesign. (2) Relation extraction models are easily disturbed by noise from words with weak relevance to the relation extraction task, leading to difficulties in correctly identifying entities and their relations. In this paper, we propose the Relation extraction method based on the Entity Attention network and Cascade binary Tagging framework (REACT). We decompose the relation extraction task into two subtasks: head entity identification and tail entity and relation identification. REACT first identifies the head entity and then identifies all possible tail entities that can be paired with the head entity, as well as all possible relations. With this architecture, the model can handle the relation overlapping problem. In order to reduce the interference of words in the text that are not related to the head entity or relation extraction task and improve the accuracy of identifying the tail entities and relations, we designed an entity attention network. To demonstrate the effectiveness of REACT, we construct a high-quality Chinese dataset and conduct a large number of experiments on this dataset. The experimental results fully confirm the effectiveness of REACT, showing its significant advantages in handling the relation overlapping problem compared to current other methods. relation extraction; relation overlapping problem; attention mechanism; gate mechanism +
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there are shared entities among the entity-relation triad in the text. According to the types of overlapping, the relation overlapping problem can be further divided into three subcategories: Normal, Single Entity Overlap (SEO), and Entity Pair Overlap (EPO), as illustrated in the examples in Figure 1.  
Most existing relation extraction methods cannot effectively deal with the relation overlapping problem for two main reasons: (1) The model design is flawed and does not consider the situation where one entity in the text may have relations with multiple entities ([PERSON] et al. [4]), or it does not consider the existence of multiple relations between a particular head and tail entity situation ([PERSON] et al. [11]). (2) When the relation overlapping problem arises, there are always numerous entities in the text, and most of these entities do not have relations with each other. The noise interference from these irrelevant entities may mislead the model, causing it to incorrectly identify these irrelevant entities as part of the tail entity or relation. Additionally, the model needs to accurately understand the context around the head entity in order to correctly identify the tail entity and relation. The presence of irrelevant entities increases the difficulty of understanding the context. As shown in Figure 2, the head entity \"[PERSON]\" in the text is only able to form the entity-relation triad \"{[PERSON], Founder, Jinghai Enterprises}\" with the tail entity \"Jinghai Enterprises\", and there are no relations with other entities. Due to the interference of entities unrelated to \"[PERSON]\", it is difficult for the model to identify the tail entities related to the head entity \"[PERSON]\" and the relations between them. Figure 1: Examples of cases of Normal, Single Entity Overlap (SEO), and Entity Pair Overlap (EPO). The overlapping entities are marked in bold. The first example belongs to the Normal class, which has no overlapping entities.