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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