# CRSAtt: By Capturing Relational Span and Using Attention for Relation Classification
## 5 Conclusions  
In recent years, Bert-based relation classification models have become increasingly popular with the rise of pre-trained models. However, these models cannot focus well on the semantics of the relationships between entities and rely only on the powerful feature extraction ability of the Bert model for relation classification. Therefore, in order to enable the model to focus on the semantics of relationships between entities, as well as to make full use of various feature information that facilitates relation classification. We propose a new relation classification model, CRSAtt, for solving the problem whereby existing models cannot accurately extract the semantics of relationships between entities by sentence-level features alone. We intercept the span between entities in a sentence during data pre  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Precision** & **Recall** & **f1** \\ \hline CRSAtt\_NO\_SRS & 88.46 & 89.79 & 89.12 \\ CISAtt & **88.78** & **91.60** & **90.17** \\ \hline \hline \end{tabular}  
* The value in bold denotes the highest value of this evaluation metric.  
\end{table}
Table 10: Comparison of the model without SRS feature fusion and the full model on the SemEval-2010 Task 8 dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Precision** & **Recall** & **f1** \\ \hline CRSAtt\_NO\_PR & 87.62 & 90.99 & 89.27 \\ CSAtt & **88.78** & **91.60** & **90.17** \\ \hline \hline \end{tabular}  
* The value in bold denotes the highest value of this evaluation metric.  
\end{table}
Table 11: Comparison of the model without prediction structure and the full model on the SemEval-2010 Task 8 dataset.  
processing and use it as the semantics of the relationship between entities. The sentence and span are fed into the feature extraction architecture to extract various features that are fed into the attention-based prediction structure for relation classification. In order to have a better grasp of the semantic information of the relationships between entities, we propose a feature fusion method called SRS (Strengthen Relational Semantics), which aims to integrate global information and local relational information. In the prediction structure, we make our model perform particularly well on the recall metric by cleverly designing the way q and k are constructed. Experiments on the SemEval-2010 Task 8 dataset showed that the CRSAtt model improved performance over existing methods with an f1 score of 90.55%. In addition, the results of the ablation study on the SemEval-2010 Task 8 dataset show that our proposed SRS and attention-based prediction structures have a positive impact on the classification performance of the model.  
In future work, the relation classification model needs to further improve its generalizability; so, we will train the model using a remotely supervised approach based on the research in this paper, focusing on introducing external knowledge with an aim to improve the generalizability and classification performance of the model using a large amount of data that do not require manual annotation.  
Conceptualization, M.L. and C.S.; methodology, G.L.; software, G.L.; validation, M.L., C.S. and G.L.; formal analysis, M.L.; investigation, M.L.; resources, C.S.; data curation, C.S.; writing--original draft preparation, G.L.; writing--review and editing, M.Z.; visualization, D.H.; supervision, M.Z. and D.H.; project administration, M.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key R&D Plan of China (2022 YFF0608000).  
Not applicable.  
The authors declare no conflict of interest.