| Description |
1 online resource |
| Contents |
Intro -- Preface -- Acknowledgements -- Introduction -- Contents -- About the Authors -- Acronyms -- 1 Theoretical Basis -- 1.1 Research Purposes -- 1.2 Future Directions -- 1.2.1 Trends in Research and Development -- 1.2.2 Long-Distance Dependencies -- 1.2.3 Location Information Awareness Capability -- 1.2.4 Dataset Noise Problem -- 1.3 Purpose and Significance of the NER -- 1.4 Current Status and Trends -- 1.4.1 Research Trends -- 1.4.2 Previous Research Work -- References -- 2 Related Existed Models -- 2.1 Word Embedding -- 2.1.1 One-Hot Encoding -- 2.1.2 Word2Vec -- 2.1.3 Glove |
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2.1.4 CoVe -- 2.1.5 ELMo -- 2.2 Conditional Random Fields (CRF) -- 2.3 Deep Neural Networks -- 2.3.1 Long Short-Term Memory (LSTM) -- 2.3.2 Transformers -- 2.3.3 The Pretrained BERT Model -- 2.4 The Task Description -- 2.4.1 The Purpose of the Task -- 2.4.2 The Problems of Chinese Named Entity Recognition -- 2.4.3 The Characteristics of Chinese Medical NER -- 2.5 Evaluation Indexes -- References -- 3 Medical Named Entity Recognition Models with the Attention Distraction Mechanism -- 3.1 General Framework -- 3.2 Research Targeted Problem -- 3.3 Improved Neural Network Models |
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3.3.1 Extended Input Units -- 3.3.2 Bi-SC-LSTM -- 3.3.3 Attention Distraction Mechanisms -- 3.3.4 Decoding Layer -- 3.4 Experiments and Simulation Results -- 3.4.1 Datasets -- 3.4.2 Experiments -- 3.4.3 Introduction to Comparison Models -- 3.4.4 Experimental Results -- 3.4.5 Experiment Analysis -- 3.5 Conclusions -- References -- 4 Transformer Entity Automatic Extraction Models in Multi-layer Soft Location Matching Format -- 4.1 General Framework -- 4.2 Research Targeted Problem -- 4.3 Multilayer Soft Position Matching Format Transformer -- 4.3.1 WordPiece Word Segmentation -- 4.3.2 BERT |
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4.4 The Lattice Transformer -- 4.4.1 The General -- 4.4.2 The Word Lattice Structure Transformer -- 4.5 Multi-layer Soft Position Matching -- 4.6 Fuzzy CRF -- 4.7 Experimental Setup and Simulation Results -- 4.7.1 Dataset -- 4.7.2 Pretrained Embedding and Modelling -- 4.7.3 Experimental Results -- 4.7.4 Experiment Analysis -- 4.8 Conclusion -- References -- 5 Medical Named Entity Recognition Modelling Based on Remote Monitoring and Denoising -- 5.1 A General Framework -- 5.2 Research Targeted Problems -- 5.3 Methods -- 5.3.1 Positive Sample and Unlabeled Learning Based on Category Risk Prediction |
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5.3.2 Negative Sampling Based on Positive/Negative Entity Probabilities -- 5.3.3 Encoding Modelling -- 5.4 Experimental Setup and Simulation Results -- 5.4.1 Datasets -- 5.4.2 Experimental Setup -- 5.4.3 Simulation/Experiments Results -- 5.5 Conclusions -- References -- Outlook -- Appendix |
| Summary |
This book introduces how to enhance the context capture ability of the model, improve the position information perception ability of the pretrained models, and identify and denoise the unlabeled entities. The Chinese medical named entity recognition is an important branch of the intelligent medicine, which is beneficial to mine the information hidden in medical texts and provide the medical entity information for clinical medical decision-making and medical classification. Researchers, engineers and post-graduate students in the fields of medicine management and software engineering |
| Analysis |
Medicine |
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Medical |
| Bibliography |
Includes bibliographical references |
| Notes |
Online resource; title from PDF title page (SpringerLink, viewed June 15, 2023) |
| Subject |
Natural language processing (Computer science)
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Medical informatics -- China
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Text data mining -- China
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Medical informatics
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Natural language processing (Computer science)
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Text data mining
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Tractament del llenguatge natural (Informàtica)
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Informàtica mèdica.
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China
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Xina.
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| Genre/Form |
Electronic books
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Llibres electrònics.
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| Form |
Electronic book
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| Author |
Han, Lina
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Yang, Wentao
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| ISBN |
9789819926657 |
|
9819926653 |
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