## 5 Conclusions  
In this paper, a relationship extraction model for the grid field was designed through combining semantic dependency and lexical embedding using the RoBERTa model. The text context depth characterization information was obtained through the RoBERTa pre-training model. The lexical and semantic dependency information were embedded in the RoBERTa embedding layer, weights were set for the two types of information, and the fused weights were automatically learned based on the model optimization for effective embedding. The cross-entropy function was used for training. The model effectively enhanced the deep semantic characterization ability, which improved the accuracy of relationship recognition between the different entities. The efficiency and superiority of the proposed approach were then verified on a relational extraction dataset which was curated within the grid domain. The obtained results can be summarized as follows:  
1. The combination of the semantic dependency and lexical embedding in the RoBERTa model improved the F1 value by 2.76% compared with the original BERT model. This indicated that the semantic dependency and lexical embedding effectively enhanced the relationship extraction accuracy. 2. Model 4, which used the original RoBERTa, exhibited only a 0.78% discrepancy compared with Model 5, which incorporated embedded lexical and semantic dependency. On the other hand, Model 8, which did not use any embedding enhancement, had a \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Index** & **RoBERTa** & **BERT** & **Pos\_id** & **SemDep\_id** & **CE** & **F1-Score/\%** \\ \hline
1 & ✓ & & ✓ & ✓ & ✓ & 87.92\% \\
2 & ✓ & & ✓ & & ✓ & 87.16\% \\
3 & ✓ & & & ✓ & ✓ & 87.41\% \\
4 & ✓ & & & & ✓ & 86.84\% \\
5 & & ✓ & ✓ & ✓ & ✓ & 85.96\% \\
6 & & ✓ & ✓ & & ✓ & 85.47\% \\
7 & & ✓ & & ✓ & ✓ & 85.66\% \\
8 & & ✓ & & & ✓ & 85.16\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Ablation experiments, where Pos_id denotes the addition of a lexical embedding vector SemDep_id denotes the addition of a semantic dependency embedding vector, and CE denotes the cross-entropy loss function. A check mark indicates that the module is used. 1.68% decrease in performance compared with Model 4. The inclusion of lexical and semantic dependent embeddings effectively narrowed the gap between RoBERTa and BERT, and enhanced the deep characterization ability of the model. 3. To label the relationship as quintuple spanning pairs and relationships, one entity should be selected as the main entity in the labeled data. The remaining entities should be enumerated as guest entities. The main entity and guest entities should contain entity type information. The generated main entity and guest entity set, along with the text information, can be used as a training set. Different subjects should be classified into different groups for parallel training, which strengthens the connection between the entities. In future work, we aim to use the trained model to extract information from the data provided by the power grid. A domain knowledge graph will be constructed to manage the data in an appropriate manner. Through leveraging the graph, the ability to extract valuable insights within the grid domain can be enhanced, and well-informed decisions for grid-related enterprises can be made. Furthermore, the evaluation of the capacity of the model for generalization through extending its application to other domains is of interest. Methodology, Q.M. ; writing--original draft preparation, Q.M. ; Conceptualization, X.Z. ; Formal analysis, X.Z. ; writing--review and editing, Y.D. ; visualization, Y.C. ; software (Re_Project, v.2023.09.18), D.L. ; resources, X.Z. ; data curation, D.L. and Y.C. ; supervision, Y.D. All authors have read and agreed to the published version of the manuscript. This research was funded by the Guangxi Scientific Research and Technology Development Plan Project (Grant No. AA20302002-3) and Innovation Project of China Southern Power Grid Co., Ltd. (202201161).