001468355 000__ 05463cam\\22006377a\4500
001468355 001__ 1468355
001468355 003__ OCoLC
001468355 005__ 20230707003248.0
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001468355 019__ $$a1381097090
001468355 020__ $$a9789819926657$$q(electronic bk.)
001468355 020__ $$a9819926653$$q(electronic bk.)
001468355 020__ $$z9819926645
001468355 020__ $$z9789819926640
001468355 0247_ $$a10.1007/978-981-99-2665-7$$2doi
001468355 035__ $$aSP(OCoLC)1380825732
001468355 040__ $$aYDX$$beng$$cYDX$$dGW5XE$$dEBLCP
001468355 049__ $$aISEA
001468355 050_4 $$aQP76.9.N38
001468355 08204 $$a006.3/5
001468355 1001_ $$aGuo, Shuli.
001468355 24510 $$aClinical Chinese named entity recognition in natural language processing /$$cShuli Guo, Lina Han, Wentao Yang.
001468355 260__ $$aSingapore :$$bSpringer,$$c2023.
001468355 300__ $$a1 online resource
001468355 504__ $$aIncludes bibliographical references.
001468355 5050_ $$aIntro -- Preface -- Acknowledgements -- Introduction -- Contents -- About the Authors -- Acronyms -- 1 Theoretical Basis -- 1.1 Research Purposes -- 1.2 Future Directions -- 1.2.1 Trends in Research and Development -- 1.2.2 Long-Distance Dependencies -- 1.2.3 Location Information Awareness Capability -- 1.2.4 Dataset Noise Problem -- 1.3 Purpose and Significance of the NER -- 1.4 Current Status and Trends -- 1.4.1 Research Trends -- 1.4.2 Previous Research Work -- References -- 2 Related Existed Models -- 2.1 Word Embedding -- 2.1.1 One-Hot Encoding -- 2.1.2 Word2Vec -- 2.1.3 Glove
001468355 5058_ $$a2.1.4 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
001468355 5058_ $$a3.3.1 Extended Input Units -- 3.3.2 Bi-SC-LSTM -- 3.3.3 Attention Distraction Mechanisms -- 3.3.4 Decoding Layer -- 3.4 Experiments and Simulation Results -- 3.4.1 Datasets -- 3.4.2 Experiments -- 3.4.3 Introduction to Comparison Models -- 3.4.4 Experimental Results -- 3.4.5 Experiment Analysis -- 3.5 Conclusions -- References -- 4 Transformer Entity Automatic Extraction Models in Multi-layer Soft Location Matching Format -- 4.1 General Framework -- 4.2 Research Targeted Problem -- 4.3 Multilayer Soft Position Matching Format Transformer -- 4.3.1 WordPiece Word Segmentation -- 4.3.2 BERT
001468355 5058_ $$a4.4 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
001468355 5058_ $$a5.3.2 Negative Sampling Based on Positive/Negative Entity Probabilities -- 5.3.3 Encoding Modelling -- 5.4 Experimental Setup and Simulation Results -- 5.4.1 Datasets -- 5.4.2 Experimental Setup -- 5.4.3 Simulation/Experiments Results -- 5.5 Conclusions -- References -- Outlook -- Appendix
001468355 506__ $$aAccess limited to authorized users.
001468355 520__ $$aThis book introduces how to enhance the context capture ability of the model, improve the position information perception ability of the pretrained models, and identify and denoise the unlabeled entities. The Chinese medical named entity recognition is an important branch of the intelligent medicine, which is beneficial to mine the information hidden in medical texts and provide the medical entity information for clinical medical decision-making and medical classification. Researchers, engineers and post-graduate students in the fields of medicine management and software engineering.
001468355 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed June 15, 2023).
001468355 650_0 $$aNatural language processing (Computer science)
001468355 650_0 $$aMedical informatics$$zChina.
001468355 650_0 $$aText data mining$$zChina.
001468355 655_0 $$aElectronic books.
001468355 7001_ $$aHan, Lina.
001468355 7001_ $$aYang, Wentao.
001468355 77608 $$iPrint version: $$z9819926645$$z9789819926640$$w(OCoLC)1375059147
001468355 852__ $$bebk
001468355 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-99-2665-7$$zOnline Access$$91397441.1
001468355 909CO $$ooai:library.usi.edu:1468355$$pGLOBAL_SET
001468355 980__ $$aBIB
001468355 980__ $$aEBOOK
001468355 982__ $$aEbook
001468355 983__ $$aOnline
001468355 994__ $$a92$$bISE