Table 6

Performance by sentences.

Number of sentence boundaries between arguments


inter-

intra-

inter- and intra-sentential


Relation

Metric

1 n 5

n <1

n 1

n 2

n 3

n 4

n 5


has_finding

P

24

68

65

62

60

61

61

R

18

89

81

79

78

78

77

F1

18

76

72

69

67

68

67


has_indication

P

18

49

42

42

36

32

30

R

17

59

47

42

42

39

38

F1

16

51

42

39

37

34

33


has_location

P

n/a

74

72

73

72

72

72

R

n/a

83

81

81

81

82

82

F1

n/a

77

75

76

75

76

76


has_target

P

3

64

62

59

60

59

58

R

1

75

66

64

62

61

61

F1

2

68

63

61

60

60

59


laterality_modifies

P

n/a

86

84

86

86

86

87

R

n/a

89

88

88

88

87

88

F1

n/a

85

84

85

86

85

86


negation_modifies

P

n/a

80

79

79

80

80

80

R

n/a

94

93

91

93

93

93

F1

n/a

86

85

84

85

86

85


sub_location_modifies

P

n/a

89

88

88

89

89

89

R

n/a

95

95

95

95

95

95

F1

n/a

91

91

91

91

91

91


Overall

P

22

69

65

64

62

61

60

R

17

83

75

73

71

70

70

F1

19

75

69

68

66

65

65


Variation in performance, by number of sentence boundaries (n) crossed by a relationship. For all cases, the cumulative feature set +event of Table 4 was used. For the inter-sentential-only classifier 1 ≤ n ≤ 5, the score fields for some relations are marked as n/a (not applicable). This is because some relations are either absent from the inter-sentential data (i.e. only ever appear intra-sententially), or are so rare that they do not appear in all training/test folds, and so a macro-average cannot be computed across the folds.

Roberts et al. BMC Bioinformatics 2008 9(Suppl 11):S3   doi:10.1186/1471-2105-9-S11-S3

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