# An Open Relation Extraction Method for Domain Text Based on Hybrid Supervised Learning
## 2 Related Work  
Entity relation extraction is a crucial technique for building knowledge graphs. The two main types of existing supervised extraction methods [20; 21] are (1) pipelined learning and (2) joint learning. In pipeline learning, entity recognition is conducted first, followed by relation extraction between entities. This method is prone to error propagation and ignores the direct connection between the two subtasks, and the lost connection will affect the relation extraction effect. In addition, redundant information can also affect the performance of the model. To address these issues, the researchers fused the two subtasks of entity recognition and relation extraction into one task for joint learning. Joint learning is the input of a sample sentence; the entity relation triad is directly obtained by a joint extraction model. [PERSON] [22] first proposed using recurrent neural networks for entity relation extraction, which reduced error propagation by sharing parameters and thus improved the model effect. However, the model did not consider long-range dependencies between entities. [PERSON] [23] proposed a new annotation mechanism that transforms entity relation extraction into a sequence annotation problem using an end-to-end neural network model to directly extract relation triples, reducing the impact of redundant information on the model. However, this model cannot solve the problem of complex entity relation types, such as the existence of relation between one entity and multiple entities and the existence of multiple relation between the same entity pair. Although deep learning supervised extraction methods are effective in learning many features and improving model effectiveness, the models must be trained using a large amount of well-labeled data. To reduce annotation costs, remotely supervised entity relation extraction significantly reduces manual dependency and is highly portable. [PERSON] [5] first applied remotely supervised methods to the relation extraction task by aligning news domain data with entities in FreeBase and extracting text features using annotated data. However, this approach suffers from noisy data and propagation. [PERSON] [24] used the FreeBase knowledge base to inductively process relation and used heuristic rules to automatically identify relevant words for extracting relation, reducing mislabeling to a certain extent. [PERSON] [25] proposed a method that incorporates a gated recurrent unit (GRU) and an attention mechanism for remotely supervised relation extraction, which solves the poor dependency of entities in traditional models over long distances and the error labels easily generated in remote supervision; however, the performance still has a certain gap compared with supervised relation extraction. However, the accuracy of the data samples obtained by the remotely supervised approach is low, affecting the extraction performance of the model. Traditional relation extraction is based on specific relation for extraction, which is not only time-consuming for the extraction task but also costly for annotation. The emergence of bidirectional encoder representations from transformers (BERT) [26] has led to an increasing number of relation extraction models based on pre-trained models [27; 28]. [PERSON] [29] proposed a simple model of entity relation extraction based on BERT. [PERSON] [30] used BERT for interpersonal relation extraction to reduce the impact of noisy data on the model. However, BERT is not good at handling long-text data, and the domain text in relation extraction mostly has long sentences. Moreover, in the joint learning process, the model is susceptible to the accumulation and propagation of errors due to the influence of noisy data; this ultimately leads to unsatisfactory model results. All the above methods have shortcomings in one aspect, either problems of noisy data and propagation errors or being limited to dealing with long-text features.