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Table 17 Concept normalization exact match results on the core + extensions evaluation annotation set of the 30 held-out documents compared to the baseline ConceptMapper approach

From: Concept recognition as a machine translation problem

Ontology

% OpenNMT class ID (%)

% ConceptMapper class ID (%)

% ConceptMapper FN class ID (%)

% OpenNMT character (%)

% ConceptMapper character (%)

ChEBI_EXT

86*

64

26

84*

66

CL_EXT

82*

67

11

93*

84

GO_BP_EXT

80*

34

44

76*

38

GO_CC_EXT

93*

80

18

94*

84

GO_MF_EXT

69*

60

30

69*

64

MOP_EXT

92*

64

35

97*

44

NCBITaxon_EXT

83

86*

13

93*

87

PR_EXT

15*

9

28

72*

21

SO_EXT

92*

19

40

91*

22

UBERON_EXT

81*

68

29

92*

75

  1. We report both the percent exact match on the class ID level and the character level. We also report the percentage of false negatives (FN) for ConceptMapper (i.e. no class ID prediction for a given text mention). Note that the best performance between OpenNMT and ConceptMapper is bolded with an asterisk* for both class ID and character level
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BMC Bioinformatics

ISSN: 1471-2105

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