Table 2.
Results of the baseline approach (n=463) in terms of true positive, false positive, false negative, true negative, and runtime for each model.
| Model | TPa, n (%) | FPb, n (%) | FNc, n (%) | TNd, n (%) | Runtime (s) | ||
| Word_Tokenize (n=463) | |||||||
| BiLSTMe | 248 (53.6) | 36 (7.8) | 26 (5.6) | 153 (33.0) | 389.01 | ||
| GRUf | 249 (53.8) | 40 (8.6) | 25 (5.4) | 149 (32.2) | 303.88 | ||
| CNNg | 252 (54.4) | 43 (9.3) | 22 (4.8) | 146 (31.5) | 138.72 | ||
| DeepCut (n=463) | |||||||
| BiLSTM | 258 (55.7) | 47 (10.2) | 16 (3.5) | 142 (30.7) | 476.81 | ||
| GRU | 249 (53.8) | 46 (9.9) | 25 (5.4) | 143 (30.9) | 411.99 | ||
| CNN | 258 (55.7) | 49 (10.6) | 16 (3.5) | 140 (30.2) | 152.16 | ||
| AttaCut (n=463) | |||||||
| BiLSTM | 260 (56.2) | 46 (9.9) | 14 (3.0) | 143 (30.9) | 467.10 | ||
| GRU | 251 (54.2) | 40 (8.6) | 23 (5.0) | 149 (32.2) | 387.65 | ||
| CNN | 248 (53.6) | 35 (7.6) | 26 (5.6) | 154 (33.3) | 146.02 | ||
aTP: true positive.
bFP: false positive.
cFN: false negative.
dTN: true negative.
eBiLSTM: bidirectional long short-term memory.
fGRU: gated recurrent unit.
gCNN: convolutional neural network.