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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