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Table 4 Comparison results of MicroP, MicroR and MicroF measure on discharge summaries

From: A multitask bi-directional RNN model for named entity recognition on Chinese electronic medical records

Model

MicroP

MicroR

MicroF

Naive Bayes

78.07

77.91

77.99

Maximum Entropy

88.81

88.81

88.81

Support Vector Machine

90.52

90.52

90.52

Conditional Random Field [7]

93.15

93.15

93.15

Convolutional Neural Network [7]

88.64

88.64

88.64

Bi-RNN model

90.90

90.90

90.90

Transfer learning Bi-RNN model [24]

92.25

92.25

92.25

Our proposed model

93.31

93.31

93.31