## 4 Experiment and Result Analysis
### Ablation Analysis
It is worth noting that Bi-LSTM will double the vector dimension. Therefore, in order to ensure that the dimension of the entity representation vector and the local context vector between entity pairs are consistent, after removing \(\text{Bi}-\text{LSTM}_{e}\), we concatenate two identical entity representation vectors to keep the dimensions consistent with the local context representation. Compared with EINET, F1 of Model 3 decreases by 0.92% (micro-average) and 1.01% (macro-average) in named entity recognition, and it decreases by 1.17% (micro-average) and 1.29% (macro-average) in relation extraction. The experimental results show that after removing \(\text{Bi}-\text{LSTM}_{e}\), the performance of the model in terms of NER and RE is greatly reduced. A high-quality entity representation will not only improve the performance of NER, but also have a positive impact on RE. After removing the relation extraction Bi-LSTM (\(\text{Bi}-\text{LSTM}_{e}\)), we obtain Model 4. It is worth noting that in Model 4, the local context representation comes from \(\text{Bi}-\text{LSTM}_{e}\), which is shared between NER and RE. The experimental results show that compared with EINET, the F1 of Model 4 decreases by 0.59% (micro average) and 0.50% (macro average) in named entity recognition, and it decreases by 1.44% (micro average) and 1.28% (macro average) in relation extraction. It is proven that enhancing local semantic information has a significant effect on relation extraction. Compared with shared representation, separate encoding can better capture the implicit semantic features of local context. At the same time, NER is positively affected by \(\text{Bi}-\text{LSTM}_{e}\). Model 5 removes all implicit semantic enhancement information on the basis of EINET, that is, removes \(\text{Bi}-\text{LSTM}_{e}\) and \(\text{Bi}-\text{LSTM}_{e}\) at the same time. The experimental results show that compared with EINET, the F1 of Model 5 decreases by 1.12% (micro average) and 1.18% (macro average) in named entity recognition, and it decreases by 1.74% (micro average) and 1.52% (macro average) in relationship extraction. Removing the enhancement of implicit semantics leads to a large performance drop, which once again illustrates the necessity of the enhancement of implicit semantic information. To demonstrate the effectiveness of global semantic information and local context length information in relation extraction, we compare Model 6, Model 7, and Model 8 with the complete model EINET. After removing the global semantic information in relation extraction, we obtain Model 6. Compared with EINET, the F1 of Model 6 decreases by 0.30% (micro-average) and 0.41% (macro-average) in named entity recognition, and it decreases by 0.64% (micro average), 0.13% (macro average) in relationship extraction. It shows that global semantic information is helpful to improve the performance of relation extraction, and it can also promote the performance of named entity recognition. Model 7 is obtained after removing the local context length representation information. Compared with EINET, the F1 of Model 7 reduces by 0.46% (micro average) and 0.64% (macro average) in named entity recognition, and it decreases by 0.83% (micro average) and 0.39% (macro average) in relation extraction. It shows that local context length information is helpful to improve the performance of relation extraction, and it can also promote the optimization of named entity recognition. At the same time, compared with the influence of global semantic information in relation extraction, the effect of local context length information is more obvious. Model 8 removes both the global semantic information and local context length information in relation extraction. The experimental results show that compared with EINET, the F1 of Model 8 is better than the EINET, the F1 of Model 8 is better than the EINET, and the EINET, and the EINET, and the EINET, and the EINET, respectively.