# FA-RCNet: A Fused Feature Attention Network for Relationship Classification
## 4 Experiments
### Experimental Results and Analysis
#### 4.3.1 Comparative Experimental Results and Analysis  
The main comparison models in this paper on the SemEval-2010 Task8 dataset are RNN-based models with BiLSTM-Attention, Entity Attention BiLSTM, BLSTM + BTLSTM + Att. CNN models based on CR-CNN, Multi-Attention CNN, DesRC - CNN, TACNN. TRE, R - BERT, BERTEM + MTB, BERT - GMAN based on pre-trained models. F1 scores are shown in Table 6.  
As can be seen from Table 6, the model in this paper achieves good results on the SemEval-2010 Task8 dataset. The F1 score reached 90.33%, which is an improvement of 3.23%, 1.08%, 0.83%, and 0.08% for the models in this paper compared to the TRE, R-BERT, BERTEM + MTB, and BERT - GMAN models, which are also based on pre - trained models, respectively. The main comparison models in this paper on the KBP37 dataset are the more classical models RNN, CNN, BiLSTM - CNN, structured block - CNN, Att - RCNN, Bi - SDP - Att. There is also recent work proposing relational classification models such as D - BERT, LGCNN, MALNet. F1 scores are shown in Table 7.  
As can be seen from Table 7, the model in this paper also achieves good results on the KBP37 dataset. The F1 score reached 69.9%. Compared to the performance of the models LGCNN, MALNet, and D-BERT proposed in recent work on the KBP dataset, the F1 values of the models in this paper were improved by 8.55%, 6.75%, and 0.75%, respectively. The above experimental results show that the model in this paper performs separate attention operations on entity features, which are then stitched with the global information of the whole sentence. It is not only possible to enhance the semantic representation of entity \begin{table}
\begin{tabular}{l l} \hline \hline
**Model** & **F1** \\ \hline BiLSTM - Attention & 84.0\% \\ Entity Attention BiLSTM & 85.2\% \\ BLSTM + BTLSTM + Att & 87.1\% \\ Bi - LSTM + LET & 85.2\% \\ MALNet & 86.3\% \\ CR - CNN & 84.1\% \\ Multi - Attention CNN & 88.0\% \\ DesRC - CNN & 86.6\% \\ TACNN & 85.3\% \\ TRE & 87.1\% \\ R - BERT & 89.25\% \\ BERTEM + MTB & 89.5\% \\ BERT - GMAN & 90.25\% \\ \hline
**FA-RCNet (our)** & **90.33\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: A comparison test with other relational classification models on the SemEval-2010 Task8 dataset. \begin{table}
\begin{tabular}{l l} \hline \hline
**Model** & **F1** \\ \hline MALNet & 28.8\% \\ CR - CNN & 55.1\% \\ Multi - Attention CNN & 60.1\% \\ DesRC - CNN & 60.9\% \\ TACNN & 61.83\% \\ TRE & 64.39\% \\ R - BERT & 61.4\% \\ BERTEM + MTB & 63.2\% \\ BERT - GMAN & 69.2\% \\ \hline
**FA-RCNet (our)** & **69.95\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: A comparison test with other relational classification models on the KBP37 dataset. features, but also to embed entity semantics into contextual information. The semantic information in the input features of the classification network can be better enriched, which in turn improves the accuracy of the relational classification task. Compared with the CNN-based relationship classification model, FA-RCNet can overcome the shortcomings of the CNN model in terms of time span. It can just capture the relationship characteristics between long-distance entities. Compared with the RNN-based relationship classification model, FA-RCNet can overcome the disadvantage that the RNN model cannot query the correlation between the current word and each word in the sentence. FA-RCNet extracts the semantic features of each word in the sentence through the self-attention mechanism, which can obtain higher-quality feature vectors. Using high-quality feature vectors can further improve the accuracy of relation classification models. This paper also aims to demonstrate that the model is directionally sensitive. The Bi-LSTM+LET, a relational classification model based on the Bi-LSTM, was used as the comparison model. The Bi-LSTM model can identify high-quality directional features, so the Bi-LSTM-based relational classification model is ideal in terms of directional sensitivity.