# Joint Extraction of Entities and Relations Based on Enhanced Span and Gate Mechanism
## 2 Related Work
### Entity and Relation Extraction  
Entity and relation extraction is the process of extracting triplets from a given unstructured text and set of relations. It is a crucial step in information extraction tasks and a prominent research direction in NLP. The typical approach in early research, known as the pipeline method, was to first extract entities and then assign specific relations to each entity pair. For example, [PERSON] et al. [16] applied Recurrent Neural Networks (RNN) to relation extraction tasks. [PERSON] et al. [17] proposed using Convolutional Neural Networks (CNN) for relation extraction. [PERSON] et al. [18] combined CNN with Long Short-Term Memory (LSTM) networks and utilized the Shortest Dependency Path (SDP) principle to solve relation extraction problems, achieving notable results. This approach does not consider the useful information that can be generated during the execution of both tasks. Performing the two tasks separately can result in redundant entities or relations, leading to accumulating errors and information redundancy. To reduce the impact of error accumulation and information redundancy, researchers have proposed building joint models that concurrently extract entities and relations. The early joint models were mainly based on feature extraction. [PERSON] et al. [19] was the first to propose a parameter-sharing joint model in which entities and relations share the embedding layer, although the two tasks were still performed separately, resulting in entity redundancy and dependence on sequential labeling. Building upon [PERSON]'s work, [PERSON] et al. [20] improved the model by constructing an RNN model using multiple layers of bidirectional LSTMs, presenting the first truly neural network-based joint model. However, these methods required complex feature engineering and manual labeling when sharing feature vector parameters between the two tasks, resulting in lower extraction efficiency. In 2014, [PERSON] et al. [21] applied a sequence-to-sequence learning method, which uses an RNN to construct an encoder-decoder structure for handling sequence data. This method incorporated an attention mechanism to concentrate on the association between the input sequence and the decoder output sequence. Subsequently, researchers have started to use neural networks to build joint extraction models encompassing both entity and relation extraction. [PERSON] et al. [22] proposed a sequence labeling approach to address the extraction problem, simultaneously extracting entities and relations in the form of triplets using an end-to-end model. However, this model cannot handle relation overlap. Due to the need for manual annotation of unlabeled datasets for relation extraction, distant supervision techniques have been utilized in entity and relation extraction tasks. [PERSON] et al. [23] used distant supervision methods to address entity and relation extraction problems, which has gradually gained popularity in the field. Additionally, [PERSON] et al. [24] introduced the application of sentence-level attention mechanisms to mitigate the influence of inaccurate labels in distant supervision on the extraction outcomes. In recent times, scholars have concentrated their efforts on addressing the nested entities and overlapping relations in the realm of joint entity and relation extraction. [PERSON] et al. [25] proposed the RERE model, which first extracts relations and then matches the relations to subjects and objects to obtain relation triplets, effectively improving the accuracy of relation extraction. However, this model cannot handle cases involving multiple relations. Furthermore, [PERSON] et al. [26] introduced the PRGC model, which predicts potential relations and reduces the relation set to the predicted potential relation set. It then performs \"BIO\" tagging on the entities using a sentence processing module, and finally aligns the entity pairs and extracts the triplets. [PERSON] et al. [15] argued that joint entity and relation extraction can harm model performance.