Fine-tuning BERT for Joint Entity and Relation Extraction in Chinese Medical Text
TABLE V: Comparisons with different hyperparameters on the task of joint entity and relation extraction
['K', 'MASK', 'NER Precision', 'NER Recall', 'NER F{}_{1}-score', 'RC with Predicted Entities Precision', 'RC with Predicted Entities Recall', 'RC with Predicted Entities F{}_{1}-score']
, RC reaches the best F{}_{1}-score of 92.18%. When K=6 and MASK{}^{rc} use Eq. , NER achieves the best F{}_{1}-score of 96.77%. One possible reason is that MASK{}^{rc} defined in Eq. For RC, the F{}_{1}-score with K=4 is the lowest when MASK{}^{rc} uses Eq. One possible reason is that the two hyperparameters are closely related to each other. However, how they interact with each other in the focus attention model is still an open question.