# Named Entity Recognition in Online Medical Consultation Using Deep Learning
## 4 Research Design and Analysis of Results
### Experimental Results and Discussion
#### 4.4.1 Evaluative Comparison Across Methods
* As Rows 9 and 13 in Table 7 show, the F1 scores of CBERT-SCNN-Local_Context-BiLSTM-Global_Context-MCRF and CBERT-SCNN-Local_Context-BiLSTM-Global_Context-DeepFM-Fusion_Context-MCRF were 85.24% and 85.47%, respectively. The experimental results show that adding a deep factorization machine with fusion context mechanism component DeepFM-Fusion_Context to the model can better fuse the local contextual semantic representation learned by the stacked CNN with the local context mechanism component, and the global contextual semantic representation learned by BiLSTM with the global context mechanism component, further improving the performance. This is mainly because the factorization machine module in the deep factorization machine with the fusion context mechanism component can capture the low-order (first- and second-order) feature interactions between the local and global contextual semantic representations, while the deep module can capture the high-order feature interactions between the local and global contextual semantic representations to finally obtain the fusion contextual semantic representation. * As Rows 10 and 11 in Table 7 show, the F1 scores of ChatGPT-4o_26_tags and ChatGPT-4o_5_tags were 42.14% and 54.29%, respectively. The experimental results demonstrate that, under the premise of zero-shot prompting, although large language models (LLMs) have achieved remarkable success in general domains, their performance in specific domains remains suboptimal, with significant gaps compared to traditional methods. Through an in-depth analysis of the prediction results, we found that ChatGPT-4o struggles to recognize fine-grained entity categories that are uncommon in the specific domain. Even for the five common entity categories, ChatGPT-4o often generates hallucinations or makes incorrect judgments in its responses. This may be attributed to the fact that the training corpus of ChatGPT-4o is more focused on general domain generalization, lacking specialized optimization for specific domains. This finding highlights the necessity of training domain-specific entity recognition models for fine-grained named entity recognition tasks in specialized domains, as LLMs under zero-shot prompting are currently inadequate for such tasks. * As Rows 12 and 13 in Table 7 show, the F1 scores of CBERT-BiLSTM-context (SOTA) and CBERT-SCNN-Local_Context-BiLSTM-Global_Context-DeepFM-Fusion_Context t-MCRF were 84.48% and 85.47%, respectively. The data indicate that our proposed approach outperforms the current SOTA method by a significant margin of 0.99%. This is because the SOTA method only considers the extraction of global contextual semantic representation, neglecting the extraction of local contextual semantic representations. resentation. It also fails to incorporate a fusion context mechanism that can capture high-order and low-order feature interaction relationships. Furthermore, it does not use a decoding layer constraint, MCRF, to enhance the validity of the predicted tag sequence.