# Entity Relationship Extraction Based on a Multi-Neural Network Cooperation Model
## 5 Discussion  
It can be seen from Table 4 that there was little difference between the precision rate, recall rate, and F1 value, indicating that there were no overfitting phenomena in this experiment, indicating the effectiveness of this experiment.  
As can be seen from the Table 4 results, in the task of extracting entity relations from small samples, the precision rate of the proposed model was 91.0%, the recall rate was 91.6%, and the F1 value was 91.9%. The F1 value of the proposed model is 25.9% higher than the Word2 vec-BiGRU-FC model and 33.1% higher than the Word2 vec-Bilstm-FC model. For the current mainstream models, compared with Bert-BiCNN-RNN, the F1 value increased by 19%; compared with Bert-BiLSTM-FC, the F1 value increased by 18.6%; and compared with Bert-BiGRU-FC, the F1 value increased by 15.9%. It can be seen from the above results that the method proposed in this paper was greatly improved compared with other methods under the condition of small samples.  
It can be seen in Figure 6 that when the number of epochs increased to 50, the F1 value tended to be stable at around 60% with Word2 vec as the encoder. It can be seen in Figure 7 that when the number of epochs increased to 30, the F1 value tended to be stable around  
Figure 8: Relationship extraction effect of our RBF model before and after optimization. (**a**) The variation in F1 value at different epochs; (**b**) the variation in LOSS value at different epochs.  
Figure 9: Relationship extraction effect of our RBF model using different optimization algorithms. (**a**) Learning rate optimization based on the Exponential Decay algorithm; (**b**) the variation in F1 value at different epochs.  
70% with [PERSON] as the encoder. It can be concluded that under the condition of small samples, the effect of using [PERSON] as an encoder is better than using traditional Word2 vec; this may be due to the strong fitting ability of [PERSON]. In addition, using BiGRU as a decoder is better than using other models as decoder; it shows the excellent decoding ability of the BiGRU model, but the effect is still not ideal. Given this, we put forward the Roberta-BiGRU-FC model and optimized the feature fusion of the input layer and the learning rate of the model.  
It can be seen in Figure 8 that the optimized model had a good effect in the first round of epochs. The effect of the original model increased rapidly in the early stage; this may be due to the stable learning rate of the original model, but as the number of epochs increased to 30, the effect of the optimized model became better than the original model and tended to be stable around 91%. By comparing with the exponential decay algorithm in Figure 9, the result showed that the cosine annealing algorithm could better converge the proposed multi-neural network cooperation model and obtain better results, and the optimization of the feature input layer and learning rate could improve the training effect of the RBF model; our optimization of the RBF model was effective with a small data set.  
According to the above experimental results, it can be seen that, first, the small balanced dataset constructed in this paper could support the detection of the model; second, compared with other models, the RBF model proposed in this paper could achieve better results in the task of extracting entity relations from the small balanced dataset; third, we had a certain effect on the optimization of RBF model.