Distributed Representation for Traditional Chinese Medicine Herb via Deep Learning Models
Table 3: Correlation with Professionals’ judgment. Student means the correlation score with the Junior student.
['Model', 'Spearman’s R']
From the table we can see that PLLM model gives the best result, which surpasses the result of the student by over 10%. This phenomenon shows that our PLLM model can learn some useful knowledge out of the prescriptions in the dataset with unsupervised learning. An overall description of the prescription can indeed help predicting the herb. The simple model CBOW also gives a rather good result of 49.33%. The traditional LSA model doesn’t perform well in this experiment, maybe because it omits the local information of the herbs, which plays a more important role in TCM prescriptions. GloVe suffers the same problem that the objective of global part influences the representation of the local context.