## 5 Conclusions and Future Works  
In this paper, we propose a span-based joint entity and relation extraction model with enhancement of lexical features and internal connections (ER-LAC). On the basis of using BERT to obtain word embedding and span embedding, we added two types of lexical part-of-speech (POS) features to enrich the embedding representation of span and used transformer to capture the internal connection between span pairs. We conducted two types of experiments to verify the effectiveness of the proposed method. Firstly, in the ablation experiments using the SpERT model as the baseline model on which the multi-granularity lexical features were verified separately from the ablation experiments, it can be seen that the coarse-grained lexical feature has a relatively obvious improvement for entity extraction. Further enhancing the semantic representation of span, the f1 score of entity classification is significantly improved by 0.95%, while the enhancement of span embedding brings a 1.31% improvement to the f1 score of relation classification, proving that the enhancement of lexical features for span embedding is effective and also very effective for the entity-relation classification task. Secondly, we experimented with the encoder structure of the transformer alone without adding any lexical features to capture the intrinsic connection between span pairs in the relation classification task, and we can see that the f1 score of entity classification is significantly improved by 1.03%, while the enhancement of span embeddings brings a 1.29% improvement in the f1 score of relation classification. Finally, we fused all the innovations into the model to obtain the ER-LAC model with a 2.04% improvement in entity classification f1 score. The f1 score \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{3}{*}{**Model**} & \multicolumn{6}{c}{**SciERC**} \\ \cline{2-7}  & \multicolumn{3}{c}{**NER (\%)**} & \multicolumn{3}{c}{**RE (\%)**} \\ \cline{2-7}  & **Precision** & **Recall** & **F1** & **Precision** & **Recall** & **F1** \\ \hline PFN [37] & - & - & 69.9 & - & - & 53.2 \\ DyGIE [6] & - & - & 65.2 & - & - & 41.6 \\ DyGIE++ [7] & - & - & 67.50 & - & - & 48.40 \\ Cross-sentence [38] & - & - & 68.90 & - & - & 50.10 \\ SpERT\({}_{\text{SGBERT}}\)\({}^{\Delta}\)[8] & **70.87** & 69.79 & 70.33 & 53.40 & 48.54 & 50.84 \\ SpERT.PL\({}_{\text{SGBERT}}\)[39] & 69.82 & 71.25 & 70.53 & 51.94 & 50.62 & 51.25 \\ PL-Marker\({}_{\text{SGBERT}}\)[18] & - & - & 69.90 & - & - & **53.20** \\ ER-LAC\({}_{\text{SGBERT}}\) & 65.26 & **77.18** & **70.72** & 46.51 & **59.70** & 52.29 \\ \hline \hline \end{tabular} \({}^{\Delta}\) indicates that there are overlapping entities in the dataset.  
\end{table} 
Table 6: Performance of the proposed model and other SOTA models on the SciERC dataset. All the metrics use micro-averaged values. The bolded portion indicates the best result for each column. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Baseline Model**} & \multirow{2}{*}{**ER-LAC**} & \multirow{2}{*}{**Increment**} \\ \cline{2-3}  & **(SpERT)** & & & \\ \hline The number of & 108,331,782 & 118,383,920 & +9.27\% \\ parameters & 24.10 & 24.36 & +1.07\% \\ Training time (ms) & 10.54 & 11.24 & +6.64\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Complexity analysis. of relational classification was improved by 2.26%. The effectiveness of the innovation points is proved. We also conducted experiments on two other datasets, ADE and SciERC, which are more popular in the field of entity relations and compared them with the SOTA model to demonstrate the advancement of our model's innovation points as well as its usability. The experimental results showed that the two types of proposed POS features and internal connection relation classifiers were effective for entity and relation joint extraction. The proposed ER-LAC model also outperformed the other state-of-the-art models in three commonly used datasets.