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Table 5 Performances of methods on contiguous and discontiguous clinical entity under “strict” criterion on ICRC_CNER

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

Method Contiguous entity (%) Discontiguous entity (%)
Precision Recall F1-score Precision Recall F1-score
CRF 83.52 84.35 83.93 82.73 58.26 68.37
LSTM-CRF 83.35 87.35 85.30 78.70 63.62 70.36
Our method 83.57 87.75 85.61 77.17 65.76 71.01
  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)