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Table 7 Performance comparisons (F-score) with top-ranking systems on the overall-2013 dataset for DDI detection and DDI classification

From: An attention-based effective neural model for drug-drug interactions extraction

Method Team CLA DEC MEC EFF ADV INT
SVM RAIHANI [17] 71.1 81.5 73.6 69.6 77.4 52.4
Context-Vector [15] 68.4 81.8 66.9 71.3 71.4 51.6
Kim [16] 67.0 77.5 69.3 66.2 72.5 48.3
FBK-irst [11] 65.1 80.0 67.9 62.8 69.2 54.7
WBI [12] 60.9 75.9 61.8 61.0 63.2 51.0
UTurku [14] 59.4 69.9 58.2 60.0 63.0 50.7
NN joint AB-LSTM [32] 71.5 80.3 76.3 67.6 79.4 43.1
MCCNN [21] 70.2 79.0 72.2 68.2 78.2 51.0
Liu CNN [22] 69.8 70.2 69.3 77.8 48.4
Zhao SCNN [23] 68.6 77.2
Ours Att-BLSTM 77.3 84.0 77.5 76.6 85.1 57.7
  1. The listed results come from the corresponding papers. The symbol “-” denotes no corresponding values, because the related paper did not provide complete results (similarly hereinafter). “DEC” only indicates DDI detection. “CLA” indicates DDI classification. “MEC”, “EFF”, “ADV” and “INT” denote “mechanism”, “effect”, “advice” and “int” types, respectively. The highest scores are highlighted in bold