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Table 4 Performance comparison of disease normalization models using various parameters

From: A method for named entity normalization in biomedical articles: application to diseases and plants

  Development set Test set
Parameters Win Dim Method Precision Recall F-score Precision Recall F-score
5_200_CBOW 5 200 CBOW 0.740 0.918 0.819 0.684 0.896 0.776
5_200_skip 5 200 Skip-gram 0.730 0.909 0.809 0.719 0.890 0.795
5_300_CBOW 5 300 CBOW 0.738 0.918 0.818 0.674 0.892 0.767
5_300_skip 5 300 Skip-gram 0.746 0.916 0.822 0.722 0.893 0.798
5_400_CBOW 5 400 CBOW 0.730 0.918 0.813 0.661 0.878 0.754
5_400_skip 5 400 Skip-gram 0.732 0.916 0.813 0.730 0.905 0.808
7_200_CBOW 7 200 CBOW 0.738 0.919 0.819 0.676 0.891 0.769
7_200_skip 7 200 Skip-gram 0.719 0.900 0.799 0.698 0.882 0.780
7_300_CBOW 7 300 CBOW 0.709 0.911 0.798 0.662 0.880 0.756
7_300_skip 7 300 Skip-gram 0.683 0.895 0.775 0.776 0.769 0.772
7_400_CBOW 7 400 CBOW 0.702 0.898 0.788 0.632 0.850 0.725
7_400_skip 7 400 Skip-gram 0.690 0.896 0.779 0.667 0.887 0.761
8_200_CBOW 8 200 CBOW 0.710 0.907 0.797 0.706 0.891 0.788
  1. The bold font denotes the best result for each column