Transformer Entity Automatic Extraction Models in Multi-layer Soft Location Matching Format

Shuli Guo (), Lina Han and Wentao Yang
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Shuli Guo: Beijing Institute of Technology, National Key Lab of Autonomous Intelligent Unmanned Systems, School of Automation
Lina Han: The Second Medical Center National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Department of Cardiology
Wentao Yang: Beijing Institute of Technology, National Key Lab of Autonomous Intelligent Unmanned Systems, School of Automation

Chapter Chapter 4 in Clinical Chinese Named Entity Recognition in Natural Language Processing, 2023, pp 45-67 from Springer

Abstract: Abstract In this chapter, a multi-layer soft position matching format Transformer method is proposed to extract clinical medical entities. The new model is shown in Fig. 4.1. The proposed model consists of five parts: WordPiece preprocessing module, BERT module, multi-layer soft position matching module, word format Transformer, and fuzzy CRF module.

Date: 2023
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DOI: 10.1007/978-981-99-2665-7_4

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