Automatic Severity Classification of Coronary Artery Disease via Recurrent Capsule Network
TABLE IV: Class-wise Performance (in Terms of F{}_{1}-score) of Our RCN Model and Baseline Methods
['Method', 'Input', 'r:modifier (e1,e2)', 'r:negative (e2,e1)', 'r:position (e1,e2)', 'r:percentage (e1,e2)', 'r:percentage (e2,e1)']
Furthermore, we compare class-wise performance of our RCN model with baseline methods. Firstly, we clearly observe that our model achieves No.1 in three relations (i.e. r:modifier[e1,e2], r:negative[e2,e1] and r:percentage[e2,e1]) and No.2 in one relation (i.e. r:percentage[e1,e2]) among all the five relations. The F{}_{1}-scores are higher than 90.00 except for r:percentage(e1,e2) relation because of its low frequency (only 70 instances) in the training set. Secondly, we also obtain the same observation on the proposed entity type features as mentioned above that they can help improve performance of the original word embedding features.