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Table 5 Comparison of the performances of various systems on the three tasks (%)

From: Entity recognition from clinical texts via recurrent neural network

  System Method Exact F1-score Inexact F1-score
2010 LSTM + char-LSTM RNN 85.81 92.91
Tang et al (2013) [10] SSVM 85.82 92.40
Bruijin et al (2011)* [13] Semi-Markov 85.23 92.44
Kim et al (2015) [9] CRFs 84.30 -
Jiang et al (2011) [12] CRFs 83.91 91.30
  System Method Span F1-score Type Accuracy
2012 LSTM + char-LSTM RNN 92.29 86.94
Xu et al. (2013)* [15] CRFs 91.66 85.74
Tang et al. (2013) [16] CRFs + SVM 90.13 83.60
Sohn et al. (2013) [17] CRFs 87.00 76.77
Aleksandar et al. (2013) [18] CRFs 87.29 82.00
  System Method Exact F1-score Token F1-score
2014 LSTM + char-LSTM RNN 94.29 96.54
Yang et al. (2015) [20] CRFs 93.60 96.11
He et al. (2015) [22] CRFs 92.32 95.14
Liu et al. (2015) [21] CRFs + rule 91.24 94.64
Dehghan et al. (2015) [23] CRFs + rule 91.13 95.31