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Table 5 Impact of each component in the token embedding composition in terms of the F1-score (%)

From: DTranNER: biomedical named entity recognition with deep learning-based label-label transition model

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BC2GM

BC5CDR-Chemical

BC5CDR-Disease

NCBI-Disease

W2V

82.03

92.64

85.17

84.88

ELMo

83.41

93.78

86.76

88.27

ELMo + W2V(=DTranNER)

84.56

94.16

87.22

88.62

  1. Note: “W2V” is a variant model of DTranNER whose embedding layer uses only traditional context-independent token vectors (i.e., Wiki-PubMed-PMC [25]), “ELMo” is another variant model of DTranNER whose embedding layer uses only ELMo, and “ELMo + W2V” is equivalent to DTranNER
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BMC Bioinformatics

ISSN: 1471-2105

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