Using Natural Language Processing to Develop an Automated Orthodontic Diagnostic System
Table 1: Automatic evaluation of multi-label classification models.
['[EMPTY]', 'Precision', 'Recall', 'F1-score']
The performance of each model was automatically evaluated using the F1-score which is the harmonic average of the precision and the recall. The simple BoW model achieved higher performance. Unique structures such as bullets and incomplete sentences may have a negative impact on the sentence encoder. On the other hand, BoW models treat documents as a set of words, so they are not affected by the structure of the sentence.