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Table 5 Performance comparison of plant 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 0.7284 0.8939 0.8027 0.594 0.824 0.690
5_200_skip 5 200 Skip-gram 0.6821 0.8812 0.7690 0.524 0.783 0.628
5_300_CBOW 5 300 CBOW 0.7326 0.8934 0.8051 0.576 0.811 0.674
5_300_skip 5 300 Skip-gram 0.6836 0.8813 0.7699 0.533 0.787 0.635
5_400_CBOW 5 400 CBOW 0.7311 0.8940 0.8044 0.568 0.809 0.667
5_400_skip 5 400 Skip-gram 0.7164 0.8878 0.7929 0.540 0.790 0.642
7_200_CBOW 7 200 CBOW 0.7331 0.8934 0.8054 0.590 0.822 0.687
7_200_skip 7 200 Skip-gram 0.7062 0.8840 0.7852 0.521 0.774 0.623
7_300_CBOW 7 300 CBOW 0.7320 0.8933 0.8047 0.589 0.818 0.685
7_300_skip 7 300 Skip-gram 0.7067 0.8833 0.7852 0.528 0.781 0.630
7_400_CBOW 7 400 CBOW 0.7163 0.8862 0.7922 0.554 0.798 0.654
7_400_skip 7 400 Skip-gram 0.6218 0.8859 0.7706 0.525 0.786 0.629
  1. The bold font denotes the best result for each column