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Table 8 Overall performance of CBOW and Skip-gram according to the voting system: Number of unique UniProtKB entries and number of term pairs for protein/gene names that are involved in Experiment I, II, and combined (i.e. merging Experiment I and II)

From: Deep learning meets ontologies: experiments to anchor the cardiovascular disease ontology in the biomedical literature

  Voting system
Experiment Model Number of terms pairs Number of UniProtKB entries Number
FTV 		Number FTV for top three Number TV
(%)
I CBOW 1020 64 31 21 43 (67%)
II CBOW 816 63 29 21 49 (78%)
I and II
combined 		CBOW 1836 79 47 37 64 (81%)
I Skip-gram 1020 64 49 37 57 (89%)
II Skip-gram 816 63 56 51 60 (95%)
I and II
combined 		Skip-gram 1836 79 71 63 77 (97%)
  1. According to the voting system, for each model the last three columns show: the number of full term variants among the top twelve ranked candidate terms for the UniProtKB entries (Number FTV column); the number of full term variants among the top three ranked candidate terms for the UniProtKB entries (Number FTV for top three); and the number and % of term variants (i.e. FTV and/or PTV) among the top twelve ranked candidate terms for the UniProtKB entries (Number TV column)
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Journal of Biomedical Semantics

ISSN: 2041-1480