# Extraction of Joint Entity and Relationships with Soft Pruning and GlobalPointer
## 2 Related Work  
The early relationship extraction work generally adopts the pipeline method [27; 28], that is, the named entity recognition task is performed on the unstructured text first, and then the relationship extraction task is performed, that is, the entity is extracted first and then the relationship between the entity pairs is determined. While the pipeline approach is simple to use, both extraction models are quite flexible in that the entity and relationship models can use different datasets and do not require both annotated entity and relationship databases. However, this will result in the following drawbacks: (1) Error accumulation: the performance of relation extraction in the next phase will be affected by the error of entity extraction. (2) Entity redundancy: since the entities are paired in pairs initially, then the relation is categorized, and the candidate entities with no association will provide duplicate information, increasing the error rate and complexity. (3) Interaction separation: the dependency and internal relationship between the two subtasks are neglected. To overcome the above drawbacks, some scholars gradually proposed the joint extraction model. It means that only one model can be used to extract the triples in the text, which strengthens the connection between the two subtasks of entity extraction and relation extraction, and alleviates error propagation [29]. The joint extraction model for entities and relations can be categorized into two types: joint extraction models based on parameter sharing and joint extraction models based on joint decoding. The parameter sharing model is to share the parameters of the input features or internal hidden layers. [PERSON] et al. reported that a recurrent-neural-network-based joint extraction model uses a bidirectional long short-term memory network (Bi-LSTM) to encode entities first, and uses Tree-LSTM, which considers dependency tree-based information to model the relationship between entities [30]. However, this model only works at the sentence level and is only suitable for simple dependency parsing. [PERSON] et al. reported that pointer network decoding queries all entities before their position (forward query) for the current entity, and calculates the attention score [31]. [PERSON] et al. reported that a joint extraction model with a fusion copy mechanism is based on the sequence-to-sequence (Seq2 Seq) idea, but the model decoder only copies the tail bytes of the entity, resulting in the incomplete extraction of multi-byte entities [32]. The joint extraction model based on parameter sharing has no constraints on sub-models, but the interaction between the entity and relational models is weak due to the use of an independent decoding technique. To enhance the interaction between the two sub-models, a joint decoding algorithm is proposed. [PERSON] et al. reported that a unified joint extraction model, which labels entity and relation labels according to query word position \(p\), then detects entities at \(p\) and identifies entities at other positions that have relations with the former [15]. [PERSON] et al. used conditional random field (CRF) to simultaneously model entity and relationship models and obtained the output results of entity and relationship through the Viterbi decoding algorithm [33]. [PERSON] et al. reported a globally optimized end-to-end relation extraction neural model and proposed new LSTM features. Furthermore, they proposed a new method to integrate syntactic information to facilitate global learning [34]. [PERSON] et al. proposed a special label type to transform entity recognition and relation classification into a sequence labeling problem. The sentence is encoded by the encoding layer, and then the hidden layer vector is input into the decoding layer to directly obtain triples. The extraction process is not divided into two sub-processes: entity recognition and relationship classification [35]. [PERSON] et al.