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Table 2 Performances of different methods on the two datasets: CCKS2017_CNER and ICRC_CNER

From: Entity recognition in Chinese clinical text using attention-based CNN-LSTM-CRF

Dataset Method Strict (%) Relaxed (%)
Precision Recall F1-score Precision Recall F1-score
CCKS2017_CNER CRF 91.22 88.20 89.69 95.73 92.57 94.13
LSTM-CRF 90.68 89.67 90.17 95.18 94.12 94.65
Our Method 90.73 90.49 90.61 94.84 94.59 94.71
ICRC_CNER CRF 81.84 78.86 80.32 93.75 90.34 92.01
83.42 79.90 81.62 94.02 90.05 92.00
LSTM-CRF 83.55 82.26 82.90 93.80 92.35 93.07
82.71 83.30 83.00 92.77 93.42 93.09
Our Method 82.96 82.60 82.78 93.30 92.90 93.10
82.66 83.99 83.32 92.57 94.07 93.31
  1. Table 2 shows the performances of different methods on CCKS2017_CNER and ICRC_CNER, where the highest measures are in bold (the following sections also use the same way to denote the highest measures)