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Table 5 Performance comparison of the proposed and baseline methods on the MedConSult

From: Composition-driven symptom phrase recognition for Chinese medical consultation corpora

\(\varepsilon\)

Method

Macro/%

Micro/%

Precision

Recall

\(F_1\)

Precision

Recall

\(F_1\)

0.6

ComD

41.02

37.92

39.41

39.01

21.84

28.00

ComD-Character

36.23

33.37

34.74

34.13

13.62

19.47

BERT-CRF

47.17

16.57

24.52

47.16

15.32

23.13

BiLSTM-CRF

36.50

29.58

32.68

34.01

8.43

13.51

BDMM-based

8.23

12.15

9.81

5.92

8.58

7.00

Dictionary-based

13.52

7.72

9.83

13.76

7.72

9.89

0.7

ComD

33.53

30.08

31.71

31.11

17.42

22.33

ComD-Character

28.36

25.76

27.00

26.30

10.49

15.00

BERT-CRF

37.56

13.05

19.38

37.44

12.03

18.21

BiLSTM-CRF

32.00

25.80

28.57

26.18

7.51

11.67

BDMM-based

7.14

10.46

8.49

5.10

7.37

6.02

Dictionary-based

9.51

5.69

7.12

9.48

5.38

6.86

0.8

ComD

27.45

24.26

25.75

25.23

14.12

18.11

ComD-Character

21.81

19.44

20.55

19.78

7.89

11.28

BERT-CRF

27.52

9.48

14.10

27.45

8.82

13.35

BiLSTM-CRF

19.25

14.94

16.82

14.99

3.61

5.82

BDMM-based

6.17

8.77

7.25

4.22

6.15

5.00

Dictionary-based

8.65

5.14

6.44

8.72

4.94

6.31

1.0

ComD

25.05

22.62

23.77

23.37

13.08

16.78

ComD-Character

19.42

17.26

18.28

17.61

7.03

10.04

BERT-CRF

26.56

9.11

13.57

26.55

8.53

12.91

BiLSTM-CRF

15.00

12.52

13.65

12.59

3.03

4.89

BDMM-based

5.94

8.54

7.01

4.10

5.98

4.86

Dictionary-based

8.65

5.13

6.44

8.72

4.94

6.31

  1. The best result with bold font for each parameter/model/characteristic