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Table 8 Performance comparison between our approach and previous systems on the yidu-s4k dataset

From: An imConvNet-based deep learning model for Chinese medical named entity recognition

Models

P (precision)

R (recall)

F1-score

ELMo-ET-CRF [44]

83.65

87.61

85.59

ELMo-lattice-LSTM-CRF [45]

84.69

85.35

85.02

ACNN [46]

83.07

87.29

85.13

FS-TL(Ensemble) [47]

–

–

85.16

Our approach

91.30

91.45

91.38

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BMC Medical Informatics and Decision Making

ISSN: 1472-6947

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