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Table 4 Performances of LSTM and CRF-based models for the three tasks (F1-score %)

From: Entity recognition from clinical texts via recurrent neural network

Model 2010 i2b2 challenge (Concept Extraction) 2012 i2b2 challenge (Event Detection) 2014 i2b2 challenge (De-Identification)
Exact Inexact Span Type Exact Token
CRF 83.69 91.39 90.82 83.72 92.58 95.37
LSTM-BASELINE 85.36 92.58 92.20 87.74 93.30 96.05
LSTM + char-LSTM 85.81 92.91 92.29 86.94 94.29 96.54
LSTM + char-CNN 85.65 92.77 92.25 87.66 94.37 96.67
LSTM + char-LSTM + CNN 85.78 92.76 92.28 87.80 94.16 96.44