## 4 Experimental Results and Discussion
### Results and Analyses  
#### 4.3.1 Comparison Experiments  
PSLF-CTR performs positive feedback conditioning on itself, which is based on the recognition results of the PSL-TFR model, and uses the PSL calculation results as its feedback factor during the training of the PSL-TFR model. We designed comparison experiments between PSLF-CTR and BERT+FLAT+CRF and BERT+BiLSTM+CRF models in a private dataset to evaluate the competitiveness of the PSLF-CTR model in the temporal entity recognition task. (1) BERT+BiLSTM+CRF model is the baseline model of this paper. It is a BiLSTM+CRF sequence annotation model using Google's pre-trained BERT for word embedding. Figure 7: The effect of \(\lambda\) an \(\alpha\) taking value on model \(F1\). (**a**) \(\lambda\) taking the value on the PSL-TFR model, (**b**) \(\alpha\) taking the value on the PSLF-CTR model. (2) The BERT+FLAT+CRF model uses BERT to boost word vector expressions and FLAT to fuse temporal lexical features, and it finally extracts the optimal temporal expressions by CRF. We explore the impact of complex temporal text features by comparing the three experiments on complex temporal entity recognition. Finally, we evaluate the ability that uses the PSLF-CTR model to recognize complex temporal entities on a publicly available dataset. The results are shown in Tables 3 and 4. Table 3 shows that overall, the PSLF-CTR model had better recognition than the baseline and BERT+FLAT+CRF models. It had the highest \(F1\) in each type of time recognition except for the AIST. For the recognition of AIST, the BERT+FLAT+CRF model performed the best, and the PSLF-CTR model had less than 1% improvement in \(F1\) over the baseline model. The analysis of the samples shows that the main reasons are the large sample size, the obvious text features, and the short text. For the recognition of complex times such as FIST, FITL, PISTC, IISTC, and IITLC, the recognition effect of the PSLF-CTR model was improved more obviously, especially for the recognition of IISTC, the \(F1\) improvement reached 10.42%. The baseline model was less effective in recognizing these six types of time, with \(F1\) below 75% and even a PITLC below 50%. After analyzing their texts, we found the reason is that these six types of time all involve more complex temporal text features and fewer occurrences of IISTC and PITLC entities. Although the recognition effect of the BERT-FLAT-CRF model was better than that of the baseline model, there was a slight improvement compared to the baseline model for only individual lexical differences (e.g., FITL). The reason for the better performance of the PSLF-CTR model in combination with Table 5 is that the PSL-TFR model is sufficient to obtain a high accuracy rate in predicting the atomic statement \(l_{2}\) (\"\(is\)\(fuzzy(T)\)\") and the FIST, and FITL corresponds to the atomic statement \(l_{10}\), \(l_{13}\), whose logic rule is \(r_{3}:l_{6}\wedge l_{2}\wedge(\
eg l_{7})\to l_{10}\), \(r_{6}:l_{5}\wedge l_{2}\wedge(\
eg l_{4})\wedge(\
eg l_{7})\to l_{13}\), so the PSLF-CTR model can receive good feedback from PSL-TFR during the training process, thus improving the accuracy rate of identifying this type of entity. For PISTC, IISTC, PITLC, and IITLC, since the PSL-TFR model also has good accuracy in identifying atomic statements \(l_{7}\) (\"\(is\)\(multiple(T)\)\") and \(l_{3}\) (\"\(is\)\(periodic(T)\)\"), the combination of the corresponding rules \(r_{8}:l_{7}\wedge l_{6}\wedge l_{3}\to l_{15}\), \(r_{9}:l_{7}\wedge l_{5}\wedge l_{3}\to l_{16}\), \(r_{10}:l_{7}\wedge l_{6}\wedge(\
eg l_{3})\to l_{17}\) and \(r_{11}:l_{7}\wedge l_{5}\wedge(\
eg l_{3})\to l_{18}\), etc. makes the PSLF-CTR model perform better than the original model in identifying complex time-type entities. After adequately training PSLF-CTR models, we randomly selected 100 medical record data from the 300 in the public dataset for evaluation in this paper. The recognition results