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Table 2 Doc-level performance comparison over our proposed model without and with knowledge on the CDR dataset

From: Biomedical relation extraction via knowledge-enhanced reading comprehension

KBs

Model

P (%)

R (%)

F1 (%)

Without KBs

 Traditional ML

ME [8]

62.00

55.10

58.30

Kernel-based SVM [24]

53.20

69.70

60.30

NN-based ML

 Relation classification

CNN+SDP [6]

58.02

76.20

65.88

LSTM+CNN [25]

56.20

68.00

61.50

BRAN(Transformer) [3]

55.60

70.80

62.10

CNN+CNNchar [11]

57.00

68.60

62.30

GCNN [2]

52.80

66.00

58.60

 Sequence labeling

Bio-Seq(LSTM+CRF) [23]

60.00

67.50

63.50

 Reading comprehension

RC (Ours)

65.83

66.32

66.07

With KBs

 Traditional ML

    

SVM+Rules(+CTD)[26]

68.15

66.04

67.08

SVM(+CTD+SIDER+MEDI+MeSH) [9]

65.80

68.57

67.16

Kernel-based models(+CTD) [10]

60.84

76.36

67.72

SVM(+Euretos KB) [27]

73.10

67.60

70.20

NN-based ML

 Relation classification

CAN(+CTD) [7]

60.51

80.48

69.08

LSTM+CNN(+CTD) [4]

63.60

76.80

69.60

RPCNN(+CTD+SIDER+MEDI+MeSH fea) [5]

65.24

77.21

70.77

KCN(+CTD) [1]

69.65

72.98

71.28

 Reading comprehension

KRC(+DCh-Miner) (Ours)

65.33

67.17

66.23

KRC(+CTD) (Ours)

71.93

70.45

71.18

  1. ‘fea’ denotes features