# Aspect Sentiment Triplet Extraction Based on Deep Relationship Enhancement Networks
## 4 Experimental Process
### Discussion  
As shown in Tables 3 and 4, Peng-two-stage-IOG, GTS-CNN, GTS-BiLSTM, S\({}^{3}\)E\({}^{2}\), GTS-BERT, and DREN are tested in D1, and CMLA, RINANTE, Li-unified-R, Peng-two-stage-IOG, OTE-MTL, JET-BERT, and GTS-BERT are tested in D2. It can be seen that the experiments on 14 res, 14 lap, and 15 res in the DREN dataset all achieved the best results. Peng-two-stage demonstrated poor performance on D1 and D2. CMLA+, RINANTE+, and Li-unified-R had poor experimental results on D2, primarily attributed to the pipeline approach, which overlooked the correlation between aspect term extraction and opinion term extraction tasks, resulting in loss accumulation. Based on the performance of Peng-two-stage, CMLA+, RINANTE+, and Li-unified-R on the dataset, it can be inferred that the traditional two-step method of first identifying the attributed word and then identifying the sentiment of the attributed word in the context is defective. GTS-CNN/GTS-BiLSTM/GTS-BERT and S\({}^{3}\)E\({}^{2}\) use End-to-End to identify relationships between words. GTS-BERT have a better result than GTS- BiLSTM in D1. The reason for this result is that BERT has been pre-trained on large-scale data and can provide a strong language understanding ability. However, this method will overlook the importance of other information in entity recognition, such as structural and syntactic information. OTE-MTL uses a bilinear affine attention mechanism to recognize the boundary relationship between words, and JET-BERT uses distance information. However, the information used by the above methods is limited, and the task features are not fully utilized for the attribute word extraction and sentiment orientation discrimination task. So, the DREN proposed in this paper not only considers various available information comprehensively, thus enriching the expression of semantics, and improves the accuracy of entity recognition, but it also further improves the accuracy of term relationship discrimination. In the 16 res dataset, the DREN's performance is slightly less effective than that of GTS-BERT. While enhanced with deep relationships, DREN also introduces a small amount of noise, thus affecting the accuracy of the model. This paper further illustrates the effectiveness of DREN through ablation experiments on D2, as shown in Table 5, where \(G_{other}\) represents the comprehensive information of \(G_{pos}\), \(G_{depency}\), \(G_{position}\), and \(G_{distance}\). It can be seen that in the absence of \(G_{pos}\), \(G_{depency}\) \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **14 res** & **14 lap** & **15 res** & **16 res** \\ \hline CMLA & 42.79 & 33.16 & 37.01 & 41.72 \\ \hline RINANTE & 34.95 & 20.07 & 29.97 & 23.87 \\ \hline Li-unified-R & 51.00 & 42.34 & 47.82 & 44.31 \\ \hline Peng-two-stage-IOG & 51.46 & 42.87 & 52.32 & 54.21 \\ \hline OTE-MTL & 58.71 & 43.42 & 47.13 & 56.96 \\ \hline JET-BERT & 62.40 & 51.04 & 57.53 & 63.83 \\ \hline GTS-BERT & 68.81 & 55.42 & 58.60 & **67.58** \\ \hline DREN & **69.05** & **57.52** & **59.45** & 67.20 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Experimental results of the value F1 for the Dataset D2. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **14 res** & **14 lap** & **15 res** & **16 res** \\ \hline Peng-two-stage-IOG & 59.64 & 47.02 & 48.71 & 58.67 \\ \hline GTS-CNN & 65.94 & 51.38 & 56.64 & 64.73 \\ \hline GTS-BiLSTM & 64.49 & 51.30 & 56.29 & 65.56 \\ \hline S\({}^{3}\)E\({}^{2}\) & 66.74 & 52.01 & 58.66 & 66.87 \\ \hline GTS-BERT & 70.20 & 54.58 & 58.67 & **67.58** \\ \hline DREN & **72.27** & **56.75** & **61.41** & 65.84 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experimental results of the value F1 for the Dataset D1. \(G_{position}\), and \(G_{distance}\), the F1 values of the four datasets all decline to varying degrees. Compared with \(G_{pos}\), \(G_{depency}\), \(G_{position}\), and \(G_{distance}\), SWDA makes a larger contribution, thus further illustrating the effectiveness of SWDA on the model.