## 4 Discussion
### Experiment Result  
#### 4.2.1 Ablation Experiments  
Ablation experiments were conducted on the CoNLL04 dataset to verify the effectiveness of multi-level lexical features and the transformer encoder in relation classification. The SpERT model was used as the baseline model, and the proposed features and classifiers were added to the SpERT model to evaluate the effectiveness of the proposed methods. All gains in the experiments were absolute gains. Six different models were built as follows, based on the SpERT model and adding the proposed features or classifiers:  
1. SpERT+E(\(\mathbf{e}_{1}^{\mathrm{c}}\)): SpERT model with coarse-grained POS embedding in the entity classification;
2. SpERT+E(\(\mathbf{e}_{1}^{\mathrm{c}}\)): SpERT model with fine-grained POS embedding in the entity classification;
3. SpERT+E(\(\mathbf{e}_{1}^{\mathrm{c}}+\mathbf{e}_{1}^{\mathrm{f}}\)): SpERT model with coarse-grained and fine-grained POS embedding in the entity classification;
4. SpERT+R(\(\mathbf{e}_{1}^{\mathrm{c}}\)) + E(\(\mathbf{e}_{1}^{\mathrm{c}}+\mathbf{e}_{1}^{\mathrm{f}}\)): Model 3 with coarse-grained POS embedding in the relation classification;
5. SpERT+T: SpERT model with the transformer encoder;
6. SpERT+R(\(\mathbf{e}_{1}^{\mathrm{c}}\)) + E(\(\mathbf{e}_{1}^{\mathrm{c}}+\mathbf{e}_{1}^{\mathrm{f}}\)) + T Model 4 with the transformer encoder. The results of the ablation experiments are shown in Table 3, and all evaluation metrics in the experiment were calculated using macro-averaged values. It can be seen from the \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Datasets** & **Entities** & **Relations** & **Total Number of Samples** & **Cross-Validation** \\ \hline CoNLL04 & 5349 & 2048 & 1441 & NO \\ SciERC & 8089 & 4716 & 2687 & NO \\ ADE & 10,839 & 6821 & 4272 & YES \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The details of the three datasets. analysis of models 1 and 2 that the performance of the entity recognition and relation extraction subtasks can be improved by adding fine-grained and coarse-grained POS features. The impact of coarse-grained POS features is greater, and the F1 scores of entity and relation extraction are increased by 0.85% and 0.99%, respectively, compared with the baseline model. The fine-grained POS features improved the F1 scores of entity recognition by 0.31% and 0.36% relative to the baseline model. In model 3, both fine-grained and coarse-grained POS features were added in the entity-relation extraction subtask, and the results showed that adding both features to the entity subtask can improve the performance of entity recognition and relation extraction. The F1 scores were increased by 0.95% and 1.31%, respectively, compared with the baseline model. In model 4, coarse-grained POS features were added in the relation classification subtask based on model 3. The results showed that the F1 scores of model 4 were increased by 0.25% and 0.69% relative to model 3, and by 1.2% and 2.00% relative to the baseline model. This indicates that the proposed POS features can not only improve the performance of the overall task in the entity recognition subtask, but also have a promotion effect on the overall performance of the model in the relation extraction subtask. As the impact of fine-grained POS features is small, fine-grained POS features were not added separately in relation classification. Model 5 added a transformer encoder for classification in the relation classification task compared to the baseline model. The results showed that adding a transformer encoder alone can increase the F1 scores of entity recognition and relation extraction by 1.04% and 1.29%, respectively. Model 6 added a transformer encoder in the relation classification task based on model 5, which fully utilized the multi-level lexical features proposed in this paper and the transformer encoder in relation classification. The results showed that the F1 scores of model 6 were increased by 1.01% and 0.97% compared with model 5, and by 2.04% and 2.26% relative to the baseline model.