## 1 Introduction  
Currently, knowledge presents explosive growth in the world, and people pay more attention to seeking valuable information online. In the data-driven world, the primary challenge is learning and extracting valuable information from text. Knowledge graphs have been a popular tool in many research fields, helping individuals to access knowledge more intuitively [1]. Now, knowledge graphs are widely used in healthcare [2], financial services [3], intelligent transport, and other fields [4]. However, most of the information is unstructured or semi-structured data, for which the direct understanding and construction of knowledge graphs by computer systems are not possible [5]. So, natural language processing (NLP) is applied to extract valuable structured information from large amounts of unstructured or semi-structured text [6]. Entity recognition and relation extraction are two crucial subtasks of information extraction in NLP; they have been popular in identifying triplets from unstructured text in the form of <subject, relation, object> [7]. During the triplet extraction process, complex relationships within a single sentence lead to the phenomenon of multiple triplets in the same subject, object, or relation, as well as nested entities. According to the degree of entity overlap, the problem of triple overlap is divided into three categories: normal, entity pair overlap (EPO), and single-entity overlap (SEO) [8]. Entity pair overlap indicates that multiple relations are present between a pair of entities, while single-entity overlap refers to multiple relationships and entitiessharing the same entity. Many researchers choose to operate within a single sentence and assume only one entity pair per sentence, which struggles to identify overlapping triplets and nested entities effectively. Focused on the above problems of overlapping triples and nested entities, several models have been proposed to address these two issues. However, a lot of models often require entity annotations for each predefined relation in the set, which leads to redundant relation judgments. In addition, the multi-task problem also remains during the joint extraction of entities and relations. Optimizing the loss function is a key strategy in multi-task learning; many entity relation extraction models neglect the differences between input and output among subtasks, which may lead to domination by a certain task and a significant reduction in the impact of losses from other tasks. A common approach is to manually set weights, which can result in extensive experimentation and development costs. This research proposes a global pointer network based on relation prediction and loss function improvement (GPRL) to address the issues mentioned above. The GPRL involves relation filtering and adaptive loss weight adjustment. The method of relation filtering is used to solve the problem of redundant relation judgment, which includes predicting relations first, filtering out impossible redundant relations, reducing computational costs, and extracting entity relation triplets. The model has two subtasks: relation filtering and joint extraction of entities and relations, which employs a multi-task learning strategy to learn multiple tasks simultaneously through shared models. Fixed weights can also affect the model's performance because of different convergence speeds for various tasks. Therefore, this research adopted an adaptive loss weight adjustment strategy to balance the loss functions of multiple tasks, which can dynamically adjust performance based on learning speeds, difficulty levels, and effectiveness of learning. The main contributions of this research can be summarized as follows:  
1. A global pointer decoding approach is introduced in this research to address the issues of overlapping triplets and nested entities. Entity and relation information is encapsulated in a dual-head matrix, which effectively resolves the problems. 2. The global pointer-based model is expanded to implement two enhancements.