# Extraction of Joint Entity and Relationships with Soft Pruning and GlobalPointer
## 1 Introduction  
The entities and the relationships between entities are the main elements in the knowledge graph (KG) [1], and its general form is (subject entity, relation, object entity), referred to as entity-relation triplet for short. Most current knowledge graphs are stored in the form of triples, such as DBpedia [2], Free-base [3], NELL [4], Probase [5], Wikidata [6], etc. Extracting triples from unstructured text is a fundamental task of information extraction in natural language processing (NLP) and a key step in knowledge graph construction (KGC) [7, 8]. Pipeline-based relational triplet extraction approaches were used at the beginning, such as DSPT [9], and kernel methods [10]. The work is decomposed into two distinct subtasks using the pipeline approach: named entity identification (NER) [11, 12] and relationship extraction (RE) [13, 14]. This method first identifies the entities and then predicts the relationship of each entity pair. The pipeline approach has the advantages of simplicity, flexibility, and ease of implementation. However, this method tends to overlook the interactions between entities and relationships and is prone to error propagation [15]. To establish the interaction between these two sub-tasks, the joint extraction method is presented. Compared with the pipeline method, the joint extraction method integrates the information of entities and relations and effectively reduces the error propagation; thus,this method has received more and more attention from scholars. The joint extraction methods are divided into the feature-based model [16; 17; 18; 19] and the neural network-based model [20; 21]. The feature-based model requires a more complex preprocessing, which leads to the introduction of additional error information. Furthermore, feature-based models necessitate manual feature extraction, which increases the burden significantly. In contrast, feature learning based on neural network models is performed by machines without human intervention. The joint learning approach can integrate information from both because it can extract entities and relations simultaneously [22]. An ideal joint extraction system should be able to adaptively handle multiple situations, especially triple extraction where entities overlap, i.e., multiple relations share a common entity, which most methods cannot handle. An example of triple overlap is shown in Figure 1. The triple overlapping problem is separated into three categories: normal, single entity overlap (SEO), and entity pair overlap (EPO). Some researchers have studied the problem. HRL [23] introduces reinforcement learning methods into seq2 seq models to enhance interactions between entities, which resulted in a significant improvement. GraphRel [24] utilizes the graph convolutional networks (GCNs) [25] to build a relational graph, which takes into account the relation between all words. Despite the success of federated learning approaches, there are still drawbacks. First, in previous work using graph neural networks to obtain representations of nodes, the graph input to the graph convolutional network is pruned. However, this is prone to missing information and does not guarantee that the neural network can learn the potential representations of all nodes. Secondly for entity- and relation-independent decoding, if wrong entities are identified, each relation is also assigned to these entities, resulting in a large number of invalid triples. When there are multiple relationships between entity pairs, the classifier becomes confused and cannot make accurate judgments [26]. To solve the above problems, this paper proposes a joint entity and relation extraction with Soft Pruning and GlobalPointer (SGNet), whose purpose is to improve the extraction accuracy of triples. Specifically, firstly, the graph module is used to extract the node features to obtain the token's local and global information. In terms of decomposition, to avoid pruning, the graph we construct is fully connected.