# Entity Relationship Extraction Based on a Multi-Neural Network Cooperation Model
###### Abstract  
Entity relation extraction mainly extracts relations from text, which is one of the important tasks of natural language processing. At present, some special fields have insufficient data; for example, agriculture, the metallurgical industry, etc. There is a lack of an effective model for entity relationship recognition under the condition of insufficient data. Inspired by this, we constructed a suitable small balanced data set and proposed a multi-neural network collaborative model (RBF, Roberta-Bidirectional Gated Recurrent Unit-Fully Connected). In addition, we also optimized the proposed model. This model uses the Roberta model as the coding layer, which is used to extract the word-level features of the text. This model uses BiGRU (Bidirectional Gated Recurrent Unit)-FC (Fully Connected) as the decoding layer, which is used to obtain the optimal relationship of the text. To further improve the effect, the input layer is optimized by feature fusion, and the learning rate is optimized by the cosine annealing algorithm. The experimental results show that, using the small balanced data set, the F1 value of the RBF model proposed in the paper is 25.9% higher than the traditional Word2 vec-BiGRU-FC model. It is 18.6% higher than the recent Bert-BiLSTM (Bidirectional Long Short Term Memory)-FC model. The experimental results show that our model is effective.  
natural language processing; entity relationship extraction; multi-neural network cooperation 20231368120233/09/app1316812023/09/app1316812023/09/131  
(2) Aiming at the task of entity relationship extraction under the condition of a small balanced dataset, we studied several deep learning models and the effect of multi-model collaboration. Given this, this paper put forward a new multi-neural network cooperation model, namely the RBF (Roberta-BiGRU-FC) model, based on our research. The Roberta-BiGRU-FC model uses Roberta as the coding layer and BiGRU-FC as the decoding layer. The F1 value of the experimental result on the constructed small sample data set reached 89.6%.  
(3) Proposing an effective optimization method for the RBF multi-neural network cooperation model. We optimized the RBF model on the input feature layer and learning rate to improve the effect on the task of extracting entity relations from small samples. The learning rate of the entity relationship extraction model is generally fixed, and it cannot be optimized adaptively according to the transformation of the loss function. The cosine annealing algorithm can make the learning rate dynamically adjusted, and the learning rate first slows down and then accelerates to decline. Given this, this paper applied the cosine annealing algorithm to the optimization of RBF models. After optimization, the F1 value on the constructed small sample dataset reached 91.9%.  
The rest of this paper is organized as follows: the second section briefly introduces the relevant work, the third section introduces the overall architecture of the RBF model, the fourth section describes the process and results of the experiment, the fifth chapter analyzes the experimental results, and the sixth section summarizes the content of the paper and describes the next steps.