Natural Language Processing Algorithms for Normalizing Expressions of Synonymous Symptoms in Traditional Chinese Medicine

Table 7

Comparison of the BERT-Classification model with other models.

Model	Accuracy	Precision	Recall	F1-score
Jaccard similarity	0.49188	0.65251	0.54722	0.54317
Word2Vec with cosine	0.6424 ± 0.0019	0.7365 ± 0.0093	0.6906 ± 0.0036	0.6724 ± 0.0047
DNorm	0.8572 ± 0.0050	0.8694 ± 0.0087	0.8602 ± 0.0072	0.8555 ± 0.0061
Transition-based model	0.7980 ± 0.0056	0.8256 ± 0.0081	0.7970 ± 0.0051	0.7937 ± 0.0050
RNN-CNNs-CRF	0.8852 ± 0.0036	0.8755 ± 0.0035	0.8724 ± 0.0032	0.8645 ± 0.0034
BERT-based ranking	0.9264 ± 0.0057	0.9413 ± 0.0056	0.9321 ± 0.0072	0.9313 ± 0.0065
BERT-Classification	0.9300 ± 0.0019	0.9473 ± 0.0023	0.9380 ± 0.0021	0.9378 ± 0.0021

Note. The test results are expressed as mean ± SD. Each model was repeated 10 times, except for Jaccard similarity. , compared with BERT-Classification.