## 4 Experimental Results and Discussion
### Results and Analyses
#### 4.3.2 Ablation Study  
To explore the effect of logical rules on the model recognition of complex temporal text features, we design the ablation experiments on a private dataset containing PSL-TFR and BERT-TextCNN models. The experimental results for \(l_{1}\)\(\sim\)\(l_{18}\) recognition are shown in Tables 5 and 6. In Table 5, it can be seen that the difference between the two models is not significant since \(l_{1}\)\(\sim\)\(l_{7}\) does not involve logic rules, and the negative feedback mechanism has limited influence on the model. The PSL-TFR scores the highest for the recognition of text features corresponding to \(l_{1}\) atomic utterances and the lowest for the \(l_{4}\) atomic utterances, in terms of precision, recall, and \(F1\) metrics. Except for \(l_{4}\), PSL-TFR obtained high scores in precision (\(P\)). However, in recall (\(R\)), PSL-TFR performed differently for different text feature recognition and performed poorly in recognizing \(l_{4}\), \(l_{7}\). By analyzing the experimental results, we found that the number of texts satisfying the features of \"_is incomplete_(\(T\))\" and \"_is multiple_(\(T\))\" is small in the dataset, which is not conducive to the model capturing the text features. The results of \(l_{8}\)\(\sim\)\(l_{18}\) atomic statements are shown in Table 6. The model without using any logical rules for constraints performs poorly overall in recognition of complex temporal text features and only performs well in recognizing the corresponding text features corresponding to \(l_{8}\) and \(l_{11}\). The \(F1\) score of BERT-TextCNN does not exceed 50% in recognizing text features corresponding to \(l_{17}\) and \(l_{18}\). The PSL-TFR model improves in \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**BERT+BiLSTM+CRF**} & \multicolumn{3}{c}{**BERT+FLAT+CRF**} & \multicolumn{3}{c}{**PSLF-CTR**} \\
**TYPE** & \(P\) & \(R\) & \(F1\) & \(P\) & \(R\) & \(F1\) & \(P\) & \(R\) & \(F1\) \\ \hline AIST & 93.92 & 94.13 & 94.02 & 94.59 & 94.81 & **94.70** & 94.52 (0.60) & 93.45 (\(-\)0.68) & 93.98 (\(-\)0.04) \\ RIST & 91.89 & 92.73 & 92.31 & 92.17 & 92.73 & **92.45** & 91.34 (\(-\)0.55) & 92.73 (0.00) & 92.03 (\(-\)0.28) \\ FIST & 85.39 & 79.17 & 82.16 & 87.36 & 79.17 & 83.06 & 87.64 (2.25) & 81.25 (2.08) & **84.32 (2.16)** \\ \hline AITL & 84.62 & 55.00 & 66.67 & 80.00 & 60.00 & 68.57 & 92.31 (7.69) & 60.00 (5.00) & **72.73 (6.06)** \\ RITL & 68.38 & 65.03 & 66.67 & 75.76 & 69.93 & **72.73** & 76.15 (7.77) & 69.23 (4.20) & 72.53 (5.86) \\ FITL & 72.88 & 79.63 & 76.11 & 72.41 & 77.78 & 77.19 & 73.77 (0.89) & 83.33 (3.70) & **78.26 (2.15)** \\ IITL & 85.00 & 85.00 & 85.00 & 84.44 & 95.00 & **89.41** & 86.05 (1.05) & 92.50 (7.5) & 89.16 (4.16) \\ \hline PISTC & 45.71 & 52.17 & 48.73 & 46.15 & 52.17 & 48.98 & 46.60 (0.89) & 52.17 (0.00) & **49.23 (0.50)** \\ IISTC & 79.17 & 95.00 & 86.36 & 82.61 & 95.00 & 88.37 & 84.44 (5.27) & 95.00 (0.00) & **89.41 (3.05)** \\ PITLC & 50.00 & 50.00 & **50.00** & 50.00 & 50.00 & **50.00** & 50.00 (0.00) & 50.00 (0.00) & **50.00 (0.00)** \\ IITLC & 36.36 & 70.00 & 47.86 & 38.46 & 71.43 & 50.00 & 41.67 (5.31) & 71.43 (1.43) & **52.63 (4.77)** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The results of manual evaluation of the CCKS dataset. recognizing \(l_{8}\)\(\sim\)\(l_{18}\) all the corresponding text features and achieves a larger improvement in \(l_{10}\), \(l_{12}\), \(l_{14}\)\(\sim\)\(l_{18}\) feature recognition (complex time types such as RITL, FIST, and FITL). By analyzing the results of the two comparison experiments, we found that the BERT-TextCNN model often identifies FIST as AIST. At the same time, there are fewer such errors in the PSL-TFR model, which reduces the value of \(FP\) and thus improves the precision (\(P\)). Moreover, the precision (\(P\)) of \(l_{16}\) is improved more, and the analysis of the results shows that the same value of FP is reduced, but the precision (\(P\)) index is improved more because it is less numerous.