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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

SettingsBC2GMBC5CDR-ChemicalBC5CDR-DiseaseNCBI-Disease
W2V82.0392.6485.1784.88
ELMo83.4193.7886.7688.27
ELMo + W2V(=DTranNER)84.5694.1687.2288.62
  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