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 |