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Table 10 Detailed performance results achieved by the proposed MSTLTR and the other leading trigger recognition systems

From: A transfer learning model with multi-source domains for biomedical event trigger extraction

Trigger recognition system Precision Recall F1-measure
SVM feature-based System [8] 81.44 69.48 75.67
SVM-based System [31] 80.60 81.60 78.32
Neural Network-based System [32] 71.04 84.60 77.23
CNN-based Neural Network System [33] 80.67 76.76 78.67
RNN-based Neural Network System [34] 79.78 78.45 79.11
Attention-based Neural Network System [35] 81.33 79.48 80.39
Tree-base Neural Network System [11] 81.12 79.15 80.28
Convolutional Highway Neural Network System [12] 80.06 81.25 80.57
Hybrid Neural Network System [13] 80.03 81.54 80.66
Joint-GATE-Sentence Neural Network System [36] 81.58 81.08 81.33
Joint-GATE-Document Neural Network System [36] 82.11 82.53 82.32
BioBERT-based Neural Network System [37] 79.48 83.76 81.57
Our MSTLTR System 83.96 79.89 81.88