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Umfang: 1 online resource (333 pages)

Ausgabe: 1st ed.

ISBN: 9783030296650

Anmerkung: Intro -- Foreword -- Preface -- Acknowledgements -- Contents -- Part I: Fundamentals and Concepts -- Chapter 1: Real-time Linked Dataspaces: A Data Platform for Intelligent Systems Within Internet of Things-Based Smart Environm... -- 1.1 Introduction -- 1.2 Foundations -- 1.2.1 Intelligent Systems -- 1.2.2 Smart Environments -- 1.2.3 Internet of Things -- 1.2.4 Data Ecosystems -- 1.2.5 Enabling Data Ecosystem for Intelligent Systems -- 1.3 Real-time Linked Dataspaces -- 1.4 Book Overview -- 1.5 Summary -- Chapter 2: Enabling Knowledge Flows in an Intelligent Systems Data Ecosystem -- 2.1 Introduction -- 2.2 Foundations -- 2.2.1 Intelligent Systems Data Ecosystem -- 2.2.2 System of Systems -- 2.2.3 From Deterministic to Probabilistic Decisions in Intelligent Systems -- 2.2.4 Digital Twins -- 2.3 Knowledge Exchange Between Open Intelligent Systems in Dynamic Environments -- 2.4 Knowledge Value Ecosystem (KVE) Framework -- 2.5 Knowledge: Transfer and Translation -- 2.5.1 Entity-Centric Data Integration -- 2.5.2 Linked Data -- 2.5.3 Knowledge Graphs -- 2.5.4 Smart Environment Example -- 2.6 Value: Continuous and Shared -- 2.6.1 Value Disciplines -- 2.6.2 Data Network Effects -- 2.7 Ecosystem: Governance and Collaboration -- 2.7.1 From Ecology and Business to Data -- 2.7.2 The Web of Data: A Global Data Ecosystem -- 2.7.3 Ecosystem Coordination -- 2.7.4 Data Ecosystem Design -- 2.8 Iterative Boundary Crossing Process: Pay-As-You-Go -- 2.8.1 Dataspace Incremental Data Management -- 2.9 Data Platforms for Intelligent Systems Within IoT-Based Smart Environment -- 2.9.1 FAIR Data Principles -- 2.9.2 Requirements Analysis -- 2.10 Summary -- Chapter 3: Dataspaces: Fundamentals, Principles, and Techniques -- 3.1 Introduction -- 3.2 Big Data and the Long Tail of Data -- 3.3 The Changing Cost of Data Management. , 3.4 Approximate, Best-Effort, and ``Good Enough ́́Information -- 3.5 Fundamentals of Dataspaces -- 3.5.1 Definition and Principles -- 3.5.2 Comparison to Existing Approaches -- 3.6 Dataspace Support Platform -- 3.6.1 Support Services -- 3.6.2 Life Cycle -- 3.6.3 Implementations -- 3.7 Dataspace Technical Challenges -- 3.7.1 Query Answering -- 3.7.2 Introspection -- 3.7.3 Reusing Human Attention -- 3.8 Dataspace Research Challenges -- 3.9 Summary -- Chapter 4: Fundamentals of Real-time Linked Dataspaces -- 4.1 Introduction -- 4.2 Event and Stream Processing for the Internet of Things -- 4.2.1 Timeliness and Real-time Processing -- 4.3 Fundamentals of Real-time Linked Dataspaces -- 4.3.1 Foundations -- 4.3.2 Definition and Principles -- 4.3.3 Comparison -- 4.3.4 Architecture -- 4.4 A Principled Approach to Pay-As-You-Go Data Management -- 4.4.1 TBLś 5 Star Data -- 4.4.2 5 Star Pay-As-You-Go Model for Dataspace Services -- 4.5 Support Platform -- 4.5.1 Data Services -- 4.5.2 Stream and Event Processing Services -- 4.6 Suitability as a Data Platform for Intelligent Systems Within IoT-Based Smart Environments -- 4.6.1 Common Data Platform Requirements -- 4.6.2 Related Work -- 4.7 Summary -- Part II: Data Support Services -- Chapter 5: Data Support Services for Real-time Linked Dataspaces -- 5.1 Introduction -- 5.2 Pay-As-You-Go Data Support Services for Real-time Linked Dataspaces -- 5.3 5 Star Pay-As-You-Go Levels for Data Services -- 5.4 Summary -- Chapter 6: Catalog and Entity Management Service for Internet of Things-Based Smart Environments -- 6.1 Introduction -- 6.2 Working with Entity Data -- 6.3 Catalog and Entity Service Requirements for Real-time Linked Dataspaces -- 6.3.1 Real-time Linked Dataspaces -- 6.3.2 Requirements -- 6.4 Analysis of Existing Data Catalogs -- 6.5 Catalog Service -- 6.5.1 Pay-As-You-Go Service Levels. , 6.6 Entity Management Service -- 6.6.1 Pay-As-You-Go Service Levels -- 6.6.2 Entity Example -- 6.7 Access Control Service -- 6.7.1 Pay-As-You-Go Service Levels -- 6.8 Joining the Real-time Linked Dataspace -- 6.9 Summary -- Chapter 7: Querying and Searching Heterogeneous Knowledge Graphs in Real-time Linked Dataspaces -- 7.1 Introduction -- 7.2 Querying and Searching in Real-time Linked Dataspaces -- 7.2.1 Real-time Linked Dataspaces -- 7.2.2 Knowledge Graphs -- 7.2.3 Searching Versus Querying -- 7.2.4 Search and Query Service Pay-As-You-Go Service Levels -- 7.3 Search and Query over Heterogeneous Data -- 7.3.1 Data Heterogeneity -- 7.3.2 Motivational Scenario -- 7.3.3 Core Requirements for Search and Query -- 7.4 State-of-the-Art Analysis -- 7.4.1 Information Retrieval Approaches -- 7.4.2 Natural Language Approaches -- 7.4.3 Discussion -- 7.5 Design Features for Schema-Agnostic Queries -- 7.6 Summary -- Chapter 8: Enhancing the Discovery of Internet of Things-Based Data Services in Real-time Linked Dataspaces -- 8.1 Introduction -- 8.2 Discovery of Data Services in Real-time Linked Dataspaces -- 8.2.1 Real-time Linked Dataspaces -- 8.2.2 Data Service Discovery -- 8.3 Semantic Approaches for Service Discovery -- 8.3.1 Inheritance Between OWL-S Services -- 8.3.2 Topic Extraction and Formal Concept Analysis -- 8.3.3 Reasoning-Based Matching -- 8.3.4 Numerical Encoding of Ontological Concepts -- 8.3.5 Discussion -- 8.4 Formal Concept Analysis for Organizing IoT Data Service Descriptions -- 8.4.1 Definition: Formal Context -- 8.4.2 Definition: Formal Concept -- 8.4.3 Definition: Sub-concept Ordering -- 8.5 IoT-Enabled Smart Environment Use Case -- 8.6 Conclusions and Future Work -- Chapter 9: Human-in-the-Loop Tasks for Data Management, Citizen Sensing, and Actuation in Smart Environments -- 9.1 Introduction -- 9.2 The Wisdom of the Crowds. , 9.2.1 Crowdsourcing Platform -- 9.3 Challenges of Enabling Crowdsourcing -- 9.4 Approaches to Human-in-the-Loop -- 9.4.1 Augmented Algorithms and Operators -- 9.4.2 Declarative Programming -- 9.4.3 Generalised Stand-alone Platforms -- 9.5 Comparison of Existing Approaches -- 9.6 Human Task Service for Real-time Linked Dataspaces -- 9.6.1 Real-time Linked Dataspaces -- 9.6.2 Human Task Service -- 9.6.3 Pay-As-You-Go Service Levels -- 9.6.4 Applications of Human Task Service -- 9.6.5 Data Processing Pipeline -- 9.6.6 Task Data Model for Micro-tasks and Users -- 9.6.7 Spatial Task Assignment in Smart Environments -- 9.7 Summary -- Part III: Stream and Event Processing Services -- Chapter 10: Stream and Event Processing Services for Real-time Linked Dataspaces -- 10.1 Introduction -- 10.2 Pay-As-You-Go Services for Event and Stream Processing in Real-time Linked Dataspaces -- 10.3 Entity-Centric Real-time Query Service -- 10.3.1 Lambda Architecture -- 10.3.2 Entity-Centric Real-time Query Service -- 10.3.3 Pay-As-You-Go Service Levels -- 10.3.4 Service Performance -- 10.4 Summary -- Chapter 11: Quality of Service-Aware Complex Event Service Composition in Real-time Linked Dataspaces -- 11.1 Introduction -- 11.2 Complex Event Processing in Real-time Linked Dataspaces -- 11.2.1 Real-time Linked Dataspaces -- 11.2.2 Complex Event Processing -- 11.2.3 CEP Service Design -- 11.2.4 Pay-As-You-Go Service Levels -- 11.2.5 Event Service Life Cycle -- 11.3 QoS Model and Aggregation Schema -- 11.3.1 QoS Properties of Event Services -- 11.3.2 QoS Aggregation and Utility Function -- 11.3.3 Event QoS Utility Function -- 11.4 Genetic Algorithm for QoS-Aware Event Service Composition Optimisation -- 11.4.1 Population Initialisation -- 11.4.2 Genetic Encodings for Concrete Composition Plans -- 11.4.3 Crossover and Mutation Operations -- 11.4.3.1 Crossover. , 11.4.3.2 Mutation and Elitism -- 11.5 Evaluation -- 11.5.1 Part 1: Performance of the Genetic Algorithm -- 11.5.1.1 Datasets -- 11.5.1.2 QoS Utility Results and Scalability -- 11.5.1.3 Fine-Tuning the Parameters -- 11.5.2 Part 2: Validation of QoS Aggregation Rules -- 11.5.2.1 Datasets and Experiment Settings -- 11.5.2.2 Simulation Results -- 11.6 Related Work -- 11.6.1 QoS-Aware Service Composition -- 11.6.2 On-Demand Event/Stream Processing -- 11.7 Summary and Future Work -- Chapter 12: Dissemination of Internet of Things Streams in a Real-time Linked Dataspace -- 12.1 Introduction -- 12.2 Internet of Things: A Dataspace Perspective -- 12.2.1 Real-time Linked Dataspaces -- 12.3 Stream Dissemination Service -- 12.3.1 Pay-As-You-Go Service Levels -- 12.4 Point-to-Point Linked Data Stream Dissemination -- 12.4.1 TP-Automata for Pattern Matching -- 12.5 Linked Data Stream Dissemination via Wireless Broadcast -- 12.5.1 The Mapping Between Triples and 3D Points -- 12.5.2 3D Hilbert Curve Index -- 12.6 Experimental Evaluation -- 12.6.1 Evaluation of Point-to-Point Linked Stream Dissemination -- 12.6.2 Evaluation on Linked Stream Dissemination via Wireless Broadcast -- 12.7 Related Work -- 12.7.1 Matching -- 12.7.2 Wireless Broadcast -- 12.8 Summary and Future Work -- Chapter 13: Approximate Semantic Event Processing in Real-time Linked Dataspaces -- 13.1 Introduction -- 13.2 Approximate Event Matching in Real-time Linked Dataspaces -- 13.2.1 Real-time Linked Dataspaces -- 13.2.2 Event Processing -- 13.3 The Approximate Semantic Matching Service -- 13.3.1 Pay-As-You-Go Service Levels -- 13.3.2 Semantic Matching Models -- 13.3.3 Model I: The Approximate Event Matching Model -- 13.3.4 Model II: The Thematic Event Matching Model -- 13.4 Elements for Approximate Semantic Matching of Events -- 13.4.1 Elm 1: Sub-symbolic Distributional Event Semantics. , 13.4.2 Elm 2: Free Event Tagging.

Weitere Ausg.: Print version: Curry, Edward Real-Time Linked Dataspaces Cham : Springer International Publishing AG,c2019 ISBN 9783030296643

Sprache: Englisch

Fachgebiete: Informatik

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Umfang: 1 online resource (249 pages)

Ausgabe: 1st ed.

ISBN: 9783031548277

Anmerkung: Intro -- Foreword by Florian Schütz -- Foreword by Jan Kleijssen -- Preface -- Acknowledgments -- Contents -- List of Contributors -- Reviewers -- Acronyms -- Part I Introduction -- 1 From Deep Neural Language Models to LLMs -- 1.1 What LLMs Are and What LLMs Are Not -- 1.2 Principles of LLMs -- 1.2.1 Deep Neural Language Models -- 1.2.2 Generative Deep Neural Language Models -- 1.2.3 Generating Text -- 1.2.4 Memorization vs Generalization -- 1.2.5 Effect of the Model and Training Dataset Size -- References -- 2 Adapting LLMs to Downstream Applications -- 2.1 Prompt Optimization -- 2.2 Pre-Prompting and Implicit Prompting -- 2.3 Model Coordination: Actor-Agents -- 2.4 Integration with Tools -- 2.5 Parameter-Efficient Fine-Tuning -- 2.6 Fine-Tuning -- 2.7 Further Pretraining -- 2.8 From-Scratch Re-Training -- 2.9 Domain-Specific Distillation -- References -- 3 Overview of Existing LLM Families -- 3.1 Introduction -- 3.2 Pre-Transformer LLMs -- 3.3 BERT and Friends -- 3.4 GPT Family Proper -- 3.5 Generative Autoregressors (GPT Alternatives) -- 3.6 Compute-Optimal Models -- 3.6.1 LLaMA Family -- 3.7 Full-Transformer/Sequence-to-Sequence Models -- 3.8 Multimodal and Mixture-of-Experts Models -- 3.8.1 Multimodal Visual LLMs -- 3.8.2 Pathways Language Model, PaLM -- 3.8.3 GPT-4 and BingChat -- References -- 4 Conversational Agents -- 4.1 Introduction -- 4.2 GPT Related Conversational Agents -- 4.3 Alternative Conversational Agent LLMs -- 4.3.1 Conversational Agents Without Auxiliary Capabilities -- 4.3.2 Conversational Agents With Auxiliary Capabilities -- 4.3.2.1 Models With Non-Knowledge Auxiliary Capabilities -- 4.4 Conclusion -- References -- 5 Fundamental Limitations of Generative LLMs -- 5.1 Introduction -- 5.2 Generative LLMs Cannot Be Factual -- 5.3 Generative LLMs With Auxiliary Tools Still Struggle To Be Factual. , 5.4 Generative LLMs Will Leak Private Information -- 5.5 Generative LLMs Have Trouble With Reasoning -- 5.6 Generative LLMs Forget Fast and Have a Short Attention Span -- 5.7 Generative LLMs Are Only Aware of What They Saw at Training -- 5.8 Generative LLMs Can Generate Highly Inappropriate Texts -- 5.9 Generative LLMs Learn and Perpetrate Societal Bias -- References -- 6 Tasks for LLMs and Their Evaluation -- 6.1 Introduction -- 6.2 Natural Language Tasks -- 6.2.1 Reading Comprehension -- 6.2.2 Question Answering -- 6.2.3 Common Sense Reasoning -- 6.2.4 Natural Language Generation -- 6.3 Conclusion -- References -- Part II LLMs in Cybersecurity -- 7 Private Information Leakage in LLMs -- 7.1 Introduction -- 7.2 Information Leakage -- 7.3 Extraction -- 7.4 Jailbreaking -- 7.5 Conclusions -- References -- 8 Phishing and Social Engineering in the Age of LLMs -- 8.1 LLMs in Phishing and Social Engineering -- 8.2 Case Study: Orchestrating Large-Scale Scam Campaigns -- 8.3 Case Study: Shā Zhū Pán Attacks -- References -- 9 Vulnerabilities Introduced by LLMs Through Code Suggestions -- 9.1 Introduction -- 9.2 Relationship Between LLMs and Code Security -- 9.2.1 Vulnerabilities and Risks Introduced by LLM-Generated Code -- 9.3 Mitigating Security Concerns With LLM-Generated Code -- 9.4 Conclusion and The Path Forward -- References -- 10 LLM Controls Execution Flow Hijacking -- 10.1 Faulting Controls: The Genesis of Execution Flow Hijacking -- 10.2 Unpacking Execution Flow: LLMs' Sensitivity to User-Provided Text -- 10.3 Examples of LLMs Execution Flow Attacks -- 10.4 Securing Uncertainty: Security Challenges in LLMs -- 10.5 Security by Design: Shielding Probabilistic Execution Flows -- References -- 11 LLM-Aided Social Media Influence Operations -- 11.1 Introduction -- 11.2 Salience of LLMs -- 11.3 Potential Impact -- 11.4 Mitigation -- References. , 12 Deep(er) Web Indexing with LLMs -- 12.1 Introduction -- 12.2 Innovation Through Integration of LLMs -- 12.3 Navigating Complexities: Challenges and Mitigation Strategies -- 12.3.1 Desired Behavior of LLM-Based Search Query Creation Tools -- 12.3.2 Engineering Challenges and Mitigations -- 12.3.2.1 Ethical and Security Concerns -- 12.3.2.2 Fidelity of Query Responses and Model Accuracy -- 12.3.2.3 Linguistic and Regulatory Variations -- 12.3.2.4 Handling Ambiguous Queries -- 12.4 Key Takeaways -- 12.5 Conclusion and Reflections -- References -- Part III Tracking and Forecasting Exposure -- 13 LLM Adoption Trends and Associated Risks -- 13.1 Introduction -- 13.2 In-Context Learning vs Fine-Tuning -- 13.3 Adoption Trends -- 13.3.1 LLM Agents -- 13.4 Potential Risks -- References -- 14 The Flow of Investments in the LLM Space -- 14.1 General Context: Investments in the Sectors of AI, ML, and Text Analytics -- 14.2 Discretionary Evidence -- 14.3 Future Work with Methods Already Applied to AI and ML -- References -- 15 Insurance Outlook for LLM-Induced Risk -- 15.1 General Context of Cyber Insurance -- 15.1.1 Cyber-Risk Insurance -- 15.1.2 Cybersecurity and Breaches Costs -- 15.2 Outlook for Estimating the Insurance Premia of LLMs Cyber Insurance -- References -- 16 Copyright-Related Risks in the Creation and Useof ML/AI Systems -- 16.1 Introduction -- 16.2 Concerns of Owners of Copyrighted Works -- 16.3 Concerns of Users Who Incorporate Content Generated by ML/AI Systems Into Their Creations -- 16.4 Mitigating the Risks -- References -- 17 Monitoring Emerging Trends in LLM Research -- 17.1 Introduction -- 17.2 Background -- 17.3 Data and Methods: Noun Extraction -- 17.4 Results -- 17.4.1 Domain Experts Validation and Interpretations -- 17.5 Discussion, Limitations and Further Research -- 17.6 Conclusion -- References -- Part IV Mitigation. , 18 Enhancing Security Awareness and Education for LLMs -- 18.1 Introduction -- 18.2 Security Landscape of LLMs -- 18.3 Foundations of LLM Security Education -- 18.4 The Role of Education in Sub-Areas of LLM Deployment and Development -- 18.5 Empowering Users Against Security Breaches and Risks -- 18.6 Advanced Security Training for LLM Users -- 18.7 Conclusion and the Path Forward -- References -- 19 Towards Privacy Preserving LLMs Training -- 19.1 Introduction -- 19.2 Dataset Pre-processing with Anonymization and De-duplication -- 19.3 Differential Privacy for Fine-Tuning Models -- 19.4 Differential Privacy for Deployed Models -- 19.5 Conclusions -- References -- 20 Adversarial Evasion on LLMs -- 20.1 Introduction -- 20.2 Evasion Attacks in Image Classification -- 20.3 Impact of Evasion Attacks on the Theory of Deep Learning -- 20.4 Evasion Attacks for Language Processing and Applicability to Large Language Models -- References -- 21 Robust and Private Federated Learning on LLMs -- 21.1 Introduction -- 21.1.1 Peculiar Challenges of LLMs -- 21.2 Robustness to Malicious Clients -- 21.3 Privacy Protection of Clients' Data -- 21.4 Synthesis of Robustness and Privacy -- 21.5 Concluding Remarks -- References -- 22 LLM Detectors -- 22.1 Introduction -- 22.2 LLMs' Salience -- 22.2.1 General Detectors -- 22.2.2 Specific Detectors -- 22.3 Potential Mitigation -- 22.3.1 Watermarking -- 22.3.2 DetectGPT -- 22.3.3 Retrieval Based -- 22.4 Mitigation -- References -- 23 On-Site Deployment of LLMs -- 23.1 Introduction -- 23.2 Open-Source Development -- 23.3 Technical Solution -- 23.3.1 Serving -- 23.3.2 Quantization -- 23.3.3 Energy Costs -- 23.4 Risk Assessment -- References -- 24 LLMs Red Teaming -- 24.1 History and Evolution of Red-Teaming Large Language Models -- 24.2 Making LLMs Misbehave -- 24.3 Attacks -- 24.3.1 Classes of Attacks on Large Language Models. , 24.3.1.1 Prompt-Level Attacks -- 24.3.1.2 Contextual Limitations: A Fundamental Weakness -- 24.3.1.3 Mechanisms of Distractor and Formatting Attacks -- 24.3.1.4 The Role of Social Engineering -- 24.3.1.5 Integration of Fuzzing and Automated Machine Learning Techniques for Scalability -- 24.4 Datasets -- 24.5 Defensive Mechanisms Against Manual and Automated Attacks on LLMs -- 24.6 The Future -- Appendix -- References -- 25 Standards for LLM Security -- 25.1 Introduction -- 25.2 The Cybersecurity Landscape -- 25.2.1 MITRE CVEs -- 25.2.2 CWE -- 25.2.3 MITRE ATT& -- CK and Cyber Kill Chain -- 25.3 Existing Standards -- 25.3.1 AI RMF Playbook -- 25.3.2 OWASP Top 10 for LLMs -- 25.3.3 AI Vulnerability Database -- 25.3.4 MITRE ATLAS -- 25.4 Looking Ahead -- References -- Part V Conclusion -- 26 Exploring the Dual Role of LLMs in Cybersecurity: Threats and Defenses -- 26.1 Introduction -- 26.2 LLM Vulnerabilities -- 26.2.1 Security Concerns -- 26.2.1.1 Data Leakage -- 26.2.1.2 Toxic Content -- 26.2.1.3 Disinformation -- 26.2.2 Attack Vectors -- 26.2.2.1 Backdoor Attacks -- 26.2.2.2 Prompt Injection Attacks -- 26.2.3 Testing LLMs -- 26.3 Code Creation Using LLMs -- 26.3.1 How Secure is LLM-Generated Code? -- 26.3.2 Generating Malware -- 26.4 Shielding with LLMs -- 26.5 Conclusion -- References -- 27 Towards Safe LLMs Integration -- 27.1 Introduction -- 27.2 The Attack Surface -- 27.3 Impact -- 27.4 Mitigation -- References.

Weitere Ausg.: Print version: Kucharavy, Andrei Large Language Models in Cybersecurity Cham : Springer International Publishing AG,c2024 ISBN 9783031548260

Sprache: Englisch

Schlagwort(e): Electronic books. ; Electronic books.

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Umfang: 1 online resource (XVII, 181 p. 54 illus., 28 illus. in color.)

Ausgabe: 1st ed. 2018.

ISBN: 9783319785035 , 3319785036

Inhalt: This open access book describes the results of natural language processing and machine learning methods applied to clinical text from electronic patient records. It is divided into twelve chapters. Chapters 1-4 discuss the history and background of the original paper-based patient records, their purpose, and how they are written and structured. These initial chapters do not require any technical or medical background knowledge. The remaining eight chapters are more technical in nature and describe various medical classifications and terminologies such as ICD diagnosis codes, SNOMED CT, MeSH, UMLS, and ATC. Chapters 5-10 cover basic tools for natural language processing and information retrieval, and how to apply them to clinical text. The difference between rule-based and machine learning-based methods, as well as between supervised and unsupervised machine learning methods, are also explained. Next, ethical concerns regarding the use of sensitive patient records for research purposes are discussed, including methods for de-identifying electronic patient records and safely storing patient records. The book’s closing chapters present a number of applications in clinical text mining and summarise the lessons learned from the previous chapters. The book provides a comprehensive overview of technical issues arising in clinical text mining, and offers a valuable guide for advanced students in health informatics, computational linguistics, and information retrieval, and for researchers entering these fields.

Anmerkung: Introduction -- The history of the patient record and the paper record -- User needs: clinicians, clinical researchers and hospital management -- Characteristics of patient records and clinical corpora -- Medical classifications and terminologies -- Evaluation metrics and evaluation -- Basic building blocks for clinical text processing -- Computational methods for text analysis and text classification -- Ethics and privacy of patient records for clinical text mining research -- Applications of clinical text mining -- Networks and shared tasks in clinical text mining -- Conclusions and outlook -- References -- Index. , English

Weitere Ausg.: ISBN 9783319785028

Weitere Ausg.: ISBN 3319785028

Sprache: Englisch

DOI: 10.1007/978-3-319-78503-5

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Umfang: 1 online resource (xii, 212 pages) : , digital, PDF file(s).

ISBN: 9781316476840 (ebook)

Inhalt: Social media is an invaluable source of time-critical information during a crisis. However, emergency response and humanitarian relief organizations that would like to use this information struggle with an avalanche of social media messages that exceeds human capacity to process. Emergency managers, decision makers, and affected communities can make sense of social media through a combination of machine computation and human compassion - expressed by thousands of digital volunteers who publish, process, and summarize potentially life-saving information. This book brings together computational methods from many disciplines: natural language processing, semantic technologies, data mining, machine learning, network analysis, human-computer interaction, and information visualization, focusing on methods that are commonly used for processing social media messages under time-critical constraints, and offering more than 500 references to in-depth information.

Anmerkung: Title from publisher's bibliographic system (viewed on 04 Jul 2016). , Machine generated contents note: 1. Introduction; 2. Volume: data acquisition, storage, and retrieval; 3. Vagueness: natural language and semantics; 4. Variety: classification and clustering; 5. Virality: networks and information propagation; 6. Velocity: online methods and data streams; 7. Volunteers: humanitarian crowdsourcing; 8. Veracity: misinformation and credibility; 9. Validity: biases and pitfalls of social media data; 10. Visualization: crisis maps and beyond; 11. Values: privacy and ethics; 12. Conclusions and outlook.

Weitere Ausg.: Print version: ISBN 9781107135765

Sprache: Englisch

Schlagwort(e): Literaturbericht

URL: https://doi.org/10.1017/CBO9781316476840

URL: https://doi.org/10.1017/CBO9781316476840

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Umfang: 1 online resource (192 pages)

Ausgabe: 1st ed.

ISBN: 9783319785035

Anmerkung: Intro -- Preface -- Objectives -- Organisation of Book Chapters -- Intended Readers -- Limitations -- Book Project During Sabbatical Stay in Sydney -- Aims -- Acknowledgements -- Contents -- 1 Introduction -- 1.1 Early Work and Review Articles -- 2 The History of the Patient Record and the Paper Record -- 2.1 The Egyptians and the Greeks -- 2.2 The Arabs -- 2.3 The Swedes -- 2.4 The Paper Based Patient Record -- 2.5 Greek and Latin Used in the Patient Record -- 2.6 Summary of the History of the Patient Record and the Paper Record -- 3 User Needs: Clinicians, Clinical Researchers and Hospital Management -- 3.1 Reading and Retrieving Efficiency of Patient Records -- 3.2 Natural Language Processing on Clinical Text -- 3.3 Electronic Patient Record System -- 3.4 Different User Groups -- 3.5 Summary -- 4 Characteristics of Patient Records and Clinical Corpora -- 4.1 Patient Records -- 4.2 Pathology Reports -- 4.3 Spelling Errors in Clinical Text -- 4.4 Abbreviations -- 4.5 Acronyms -- 4.6 Assertions -- 4.6.1 Negations -- 4.6.2 Speculation and Factuality -- Levels of Certainty -- Negation and Speculations in Other Languages, Such as Chinese -- 4.7 Clinical Corpora Available -- 4.7.1 English Clinical Corpora Available -- 4.7.2 Swedish Clinical Corpora -- 4.7.3 Clinical Corpora in Other Languages than Swedish -- 4.8 Summary -- 5 Medical Classifications and Terminologies -- 5.1 International Statistical Classification of Diseases and Related Health Problems (ICD) -- 5.1.1 International Classification of Diseases for Oncology (ICD-O-3) -- 5.2 Systematized Nomenclature of Medicine: Clinical Terms (SNOMED CT) -- 5.3 Medical Subject Headings (MeSH) -- 5.4 Unified Medical Language Systems (UMLS) -- 5.5 Anatomical Therapeutic Chemical Classification (ATC) -- 5.6 Different Standards for Interoperability -- 5.6.1 Health Level 7 (HL7). , Fast Healthcare Interoperability Resources (FHIR) -- 5.6.2 OpenEHR -- 5.6.3 Mapping and Expanding Terminologies -- 5.7 Summary of Medical Classifications and Terminologies -- 6 Evaluation Metrics and Evaluation -- 6.1 Qualitative and Quantitative Evaluation -- 6.2 The Cranfield Paradigm -- 6.3 Metrics -- 6.4 Annotation -- 6.5 Inter-Annotator Agreement (IAA) -- 6.6 Confidence and Statistical Significance Testing -- 6.7 Annotation Tools -- 6.8 Gold Standard -- 6.9 Summary of Evaluation Metrics and Annotation -- 7 Basic Building Blocks for Clinical Text Processing -- 7.1 Definitions -- 7.2 Segmentation and Tokenisation -- 7.3 Morphological Processing -- 7.3.1 Lemmatisation -- 7.3.2 Stemming -- 7.3.3 Compound Splitting (Decompounding) -- 7.3.4 Abbreviation Detection and Expansion -- A Machine Learning Approach for Abbreviation Detection -- 7.3.5 Spell Checking and Spelling Error Correction -- Spell Checking of Clinical Text -- Open Source Spell Checkers -- Search Engines and Spell Checking -- 7.3.6 Part-of-Speech Tagging (POS Tagging) -- 7.4 Syntactical Analysis -- 7.4.1 Shallow Parsing (Chunking) -- 7.4.2 Grammar Tools -- 7.5 Semantic Analysis and Concept Extraction -- 7.5.1 Named Entity Recognition -- Machine Learning for Named Entity Recognition -- 7.5.2 Negation Detection -- Negation Detection Systems -- Negation Trigger Lists -- NegEx for Swedish -- NegEx for French, Spanish and German -- Machine Learning Approaches for Negation Detection -- 7.5.3 Factuality Detection -- 7.5.4 Relative Processing (Family History) -- 7.5.5 Temporal Processing -- TimeML and TIMEX3 -- HeidelTime -- i2b2 Temporal Relations Challenge -- Temporal Processing for Swedish Clinical Text -- Temporal Processing for French Clinical Text -- Temporal Processing for Portuguese Clinical Text -- 7.5.6 Relation Extraction -- 2010 i2b2/VA Challenge Relation Classification Task. , Other Approaches for Relation Extraction -- 7.5.7 Anaphora Resolution -- i2b2 Challenge in Coreference Resolution for Electronic Medical Records -- 7.6 Summary of Basic Building Blocks for Clinical Text Processing -- 8 Computational Methods for Text Analysis and Text Classification -- 8.1 Rule-Based Methods -- 8.1.1 Regular Expressions -- 8.2 Machine Learning-Based Methods -- 8.2.1 Features and Feature Selection -- Term Frequency-Inverse Document Frequency, tf-idf -- Vector Space Model -- 8.2.2 Active Learning -- 8.2.3 Pre-Annotation with Revision or Machine Assisted Annotation -- 8.2.4 Clustering -- 8.2.5 Topic Modelling -- 8.2.6 Distributional Semantics -- 8.2.7 Association Rules -- 8.3 Explaining and Understanding the Results Produced -- 8.4 Computational Linguistic Modules for Clinical Text Processing -- 8.5 NLP Tools: UIMA, GATE, NLTK etc -- 8.6 Summary of Computational Methods for Text Analysis and Text Classification -- 9 Ethics and Privacy of Patient Records for Clinical Text Mining Research -- 9.1 Ethical Permission -- 9.2 Social Security Number -- 9.3 Safe Storage -- 9.4 Automatic De-Identification of Patient Records -- 9.4.1 Density of PHI in Electronic Patient Record Text -- 9.4.2 Pseudonymisation of Electronic Patient Records -- 9.4.3 Re-Identification and Privacy -- Black Box Approach -- 9.5 Summary of Ethics and Privacy of Patient Records for Clinical Text Mining Research -- 10 Applications of Clinical Text Mining -- 10.1 Detection and Prediction of Healthcare Associated Infections (HAIs) -- 10.1.1 Healthcare Associated Infections (HAIs) -- 10.1.2 Detecting and Predicting HAI -- 10.1.3 Commercial HAI Surveillance Systems and Systems in Practical Use -- 10.2 Detection of Adverse Drug Events (ADEs) -- 10.2.1 Adverse Drug Events (ADEs) -- 10.2.2 Resources for Adverse Drug Event Detection -- 10.2.3 Passive Surveillance of ADEs. , 10.2.4 Active Surveillance of ADEs -- 10.2.5 Approaches for ADE Detection -- An Approach for Swedish Clinical Text -- An Approach for Spanish Clinical Text -- A Joint Approach for Spanish and Swedish Clinical Text -- 10.3 Suicide Prevention by Mining Electronic Patient Records -- 10.4 Mining Pathology Reports for Diagnostic Tests -- 10.4.1 The Case of the Cancer Registry of Norway -- 10.4.2 The Medical Text Extraction (Medtex) System -- 10.5 Mining for Cancer Symptoms -- 10.6 Text Summarisation and Translation of Patient Record -- 10.6.1 Summarising the Patient Record -- 10.6.2 Other Approaches in Summarising the Patient Record -- 10.6.3 Summarising Medical Scientific Text -- 10.6.4 Simplification of the Patient Record for Laypeople -- 10.7 ICD-10 Diagnosis Code Assignment and Validation -- 10.7.1 Natural Language Generation from SNOMED CT -- 10.8 Search Cohort Selection and Similar Patient Cases -- 10.8.1 Comorbidities -- 10.8.2 Information Retrieval from Electronic Patient Records -- 10.8.3 Search Engine Solr -- 10.8.4 Supporting the Clinician in an Emergency Department with the Radiology Report -- 10.8.5 Incident Reporting -- 10.8.6 Hypothesis Generation -- 10.8.7 Practical Use of SNOMED CT -- 10.8.8 ICD-10 and SNOMED CT Code Mapping -- 10.8.9 Analysing the Patient's Speech -- 10.8.10 MYCIN and Clinical Decision Support -- 10.8.11 IBM Watson Health -- 10.9 Summary of Applications of Clinical Text Mining -- 11 Networks and Shared Tasks in Clinical Text Mining -- 11.1 Conferences, Workshops and Journals -- 11.2 Summary of Networks and Shared Tasks in Clinical Text Mining -- 12 Conclusions and Outlook -- 12.1 Outcomes -- References -- Index.

Weitere Ausg.: Print version: Dalianis, Hercules Clinical Text Mining Cham : Springer International Publishing AG,c2018 ISBN 9783319785028

Sprache: Englisch

Schlagwort(e): Electronic books. ; Electronic books

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Umfang: XIX, 176 p. 171 illus., 118 illus. in color. , online resource.

Ausgabe: 1st ed. 2022.

ISBN: 9789811699955

Serie: Studies in Big Data, 104

Inhalt: This book gives a comprehensive view of graph theory in informational retrieval (IR) and natural language processing(NLP). This book provides number of graph techniques for IR and NLP applications with examples. It also provides understanding of graph theory basics, graph algorithms and networks using graph. The book is divided into three parts and contains nine chapters. The first part gives graph theory basics and graph networks, and the second part provides basics of IR with graph-based information retrieval. The third part covers IR and NLP recent and emerging applications with case studies using graph theory. This book is unique in its way as it provides a strong foundation to a beginner in applying mathematical structure graph for IR and NLP applications. All technical details that include tools and technologies used for graph algorithms and implementation in Information Retrieval and Natural Language Processing with its future scope are explained in a clear and organized format.

Anmerkung: Part A -- Chapter 1. Graph theory basics -- Chapter 2. Graph Algorithms -- Chapter 3. Networks using graph -- Part B -- Chapter 4. Information retrieval -- Chapter 5. Text document preprocessing using graph theory -- Chapter 6. Text analytics using graph theory -- Chapter 7. Knowledge graph -- Part C -- Chapter 8. Emerging Applications and development -- Chapter 9. Conclusion and future scope.

In: Springer Nature eBook

Weitere Ausg.: Printed edition: ISBN 9789811699948

Weitere Ausg.: Printed edition: ISBN 9789811699962

Weitere Ausg.: Printed edition: ISBN 9789811699979

Sprache: Englisch

DOI: 10.1007/978-981-16-9995-5

URL: https://doi.org/10.1007/978-981-16-9995-5

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Umfang: 1 Online-Ressource (xv, 526 Seiten) : , 127 Illustrationen, 74 in Farbe.

ISBN: 978-3-031-08473-7

Serie: Lecture notes in computer science 13286

Weitere Ausg.: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-08472-0

Weitere Ausg.: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-08474-4

Sprache: Englisch

Schlagwort(e): Informatik ; Natürlichsprachiges System ; Informationssystem ; Information Retrieval ; Information Extraction ; Semantic Web ; Wissensverarbeitung ; Natürliche Sprache ; Maschinelles Lernen ; Datenbankverwaltung ; Data Mining ; Anwendungssoftware ; Konferenzschrift ; Konferenzschrift ; Konferenzschrift ; Konferenzschrift

DOI: 10.1007/978-3-031-08473-7

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Umfang: 1 online resource (XVII, 398 p. 114 illus., 87 illus. in color.)

Ausgabe: 1st ed. 2025.

ISBN: 981-9670-71-3

Serie: Lecture Notes in Artificial Intelligence, 15692

Inhalt: This book constitutes the proceedings of the 17th JSAI International Symposia on Artificial Intelligence (JSAI-isAI 2025), held in Osaka, Japan, during May 26–27, 2025. The 27 full papers included in this book were carefully reviewed and selected from 82 submissions. The papers covered a wide range of topics, including AI & law, juris-informatics, natural language processing for scientific documents, Information retrieval for scientific documents, business Informatics, agent-based modeling, AI & security, AI & privacy. .

Anmerkung: -- JURISIN 2025. -- Nineteenth International Workshop on Juris-informatics (JURISIN 2025). -- Towards Accurate Legal Term Detection: Insights from Dependency Tree-based and Large Language Model Approaches. -- On the interplay between entailments among obligations and their violations. -- Legal Regulation of Knowledge Distillation: From A Trade Secret Perspective. -- Generating Guiding Principles: Evaluating Large Language Models for complex German Legal Summaries. -- On Normative Status and Argumentation Semantics of Soft-constraint based Norms. -- Hybrid AI for supporting the European Drafting Legislation. -- Labeling Case Similarity based on Co-Citation of Legal Articles in Judgment Documents with Empirical Dispute-Based Evaluation. -- Automating the Creation of Legislative Article Histories in Japanese Commercial Law: A Method for Identifying Corresponding Articles Before and After Amendments. -- PROLEG and Normative Diagrams. -- A Multilingual Legal Provision Mapping Accross Jurisdictions: A One-to-Many Approach. -- Nearest-Neighbor Search or Distance-Based Search, Which is Better for Finding Relevant Articles?. -- SCIDOCA 2025. -- Ninth International Workshop on SCIentific DOCument Analysis (SCIDOCA 2025). -- Analyzing Logical Fallacies in Large Language Models: A Study on Hallucination in Mathematical Reasoning. -- AuthNet: A Framework for Research Expert Discovery and Network Visualization Based on Topic-Specific Queries. -- Entity-Based Synthetic Data Generation for Named Entity Recognition in Low-resource Domains. -- Embedding-Based Retrieval Approaches for Automated Citation Prediction. -- An Attentigon-Driven Framework for Citation Discovery and Recommendation. -- AI-Biz 2025. -- Artificial Intelligence of and for Business (AI-Biz 2025). -- Financial Inclusion with Large Language Models: Prompt Design and Evaluation for Easy Japanese Generation. -- How students made use of an on-campus car sharing system in a university of a regional area in Japan. -- Developing an Energy Utilization Mechanism for Sustainable Societies: Household Fuel Cells (Ene-Farm), the J-Credit Scheme, and Collaborative Models. -- Analyzing the Impact of Cognitive Representation on the Performance of Post-Merger Companies. -- Research on information transmission methods in consideration of regional characteristics to promote evacuation actions during flood disasters. -- AI Security & Privacy 2025. -- First International Workshop on Artificial Intelligence Security and Privacy (AI security and Privacy 2025). -- Paradigm Shift in AI Security. -- Design of a Bachelor's Degree in Cybersecurity and Artificial Intelligence. -- Robustness bounds on the successful adversarial examples in probabilistic models: Implications from Gaussian processes. -- Empirical Evaluation of Record Reconstruction Risk from Model Explanations with Differential Privacy. -- Anomaly Detection for Multi-Tenant Networks Based on AutoEncoder. -- Cryptographic Authentication for Private RAG-Enabled LLMs.

Weitere Ausg.: ISBN 981-9670-70-5

Sprache: Englisch

DOI: 10.1007/978-981-96-7071-0

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Umfang: XXVIII, 970 p. 440 illus., 309 illus. in color. , online resource.

Ausgabe: 1st ed. 2023.

ISBN: 9783031263842

Serie: Lecture Notes in Networks and Systems, 637

Inhalt: This book describes the potential contributions of emerging technologies in different fields as well as the opportunities and challenges related to the integration of these technologies in the socio-economic sector. In this book, many latest technologies are addressed, particularly in the fields of computer science and engineering. The expected scientific papers covered state-of-the-art technologies, theoretical concepts, standards, product implementation, ongoing research projects, and innovative applications of Sustainable Development. This new technology highlights, the guiding principle of innovation for harnessing frontier technologies and taking full profit from the current technological revolution to reduce gaps that hold back truly inclusive and sustainable development. The fundamental and specific topics are Big Data Analytics, Wireless sensors, IoT, Geospatial technology, Engineering and Mechanization, Modeling Tools, Risk analytics, and preventive systems.

Anmerkung: Using Blockchain in University Management Systems- state of art -- Graph Neural Networks to improve Knowledge Graph Embedding: A survey -- Tifinagh Handwritten Character Recognition Using Machine Learning Algorithms -- Maintenance prediction based on Long Short-Term Memory algorithm -- Towards an approach for studying the evolution of learners' learning in E-learning -- Chatbots Technology and its Challenges: An Overview -- Machine Learning, Deep Neural Network and Natural Language Processing based Recommendation System -- Artificial intelligence for fake news -- Traffic congestion and road anomalies detection using CCTVs images processing, challenges & opportunities -- Text-based Sentiment analysis -- Smart tourism destinations as complex adaptive systems: A theoretical framework of resilience and sustainability -- Machine learning algorithms for automotive software defect prediction -- Agile User Stories' Driven Method: A Novel Users Stories Meta-model in the MDA Approach -- AI-based adaptive learning - State of the art -- A new Predictive analytics model to assess the employability of academic careers, based on genetic algorithms -- New approach for anomaly detection and prevention -- FunLexia: an Intelligent Game for Children with Dyslexia to Learn Arabic -- Artificial Neural Networks Cryptanalysis of Merkle-Hellman Knapsack Cryptosystem -- Using machine learning algorithms to increase the supplier selection process efficiency in supply chain 4.0 -- A new approach to intelligent-oriented analysis and design of urban traffic control: Case of a traffic light -- Spatio-temporal crime forecasting: Approaches, datasets, and comparative study -- Data migration from relational to NoSQL database : Review & Comparative study -- Recommendation system: technical study -- The appropriation of the agile approach in public sector: Modeling the achievement of good governance -- The contribution of Deep learning models: application of LSTM to predict the Moroccan GDP growth using drought indexes -- Natural Language Processing and Motivation for Language Learning -- Generating Artworks using One Class SVM with RBF kernel -- Multiobjective Evolutionary Algorithms for Engineering Design Problems -- Designing Hybrid Storage Architectures with RDBMS and NoSQL Systems: A Survey -- Analysis of the pedagogical effectiveness of teacher qualification cycle in Morocco: A Machine learning model approach -- Smart education - A case study on a simulation for climate change awareness & engagement -- Towards an E-commerce personalized recommendation system with KNN classification method -- Convolutional Long Short-Term Memory Network Model for Dynamic Texture Classification: A Case Study -- Towards an accident severity prediction system with Logistic Regression -- FUZZY C-MEANS Based Extended Isolation Forest for Anomaly Detection -- Fashion Image Classification using Convolutional Neural Network-VGG16 and eXtreme Gradient Boosting Classifier -- MentorBot: A Traceability-Based Recommendation Chatbot for Moodle -- Regularization in CNN: A mathematical study for L1, L2 and Dropout regularizers -- Shipment consolidation using K-means and a combined DBSCAN-KNN approach -- A new approach to protect Data in-Use at Document Oriented Databases -- A Dual Carriageway Smart Street Lighting Controller Based On Multi-Variate Traffic Forecast.-Blockchain-based Self Sovereign Identity Systems: high-level processing and a challenges-based comparative analysis -- Impact of Machine Learning on The Improvement of Accounting Information Quality -- NLP Methods' Information Extraction for Textual Data: An Analytical Study -- Handwriting recognition in historical manuscripts using a deep learning approach -- Artificial intelligence for a sustainable finance: A bibliometric analysis -- Geoparsing Recognition and Extraction from Amazigh corpus using The NooJ Complex Annotation Structures -- Agent-based merchandise management and real-time decision support systems -- Selecting the Best Moroccan Tourist Destination Using the Fuzzy Analytic Hierarchy Process -- Improving model performance of the prediction of online shopping using oversampling and feature selection -- Combining Descriptors for Efficient Retrieval in Databases Images -- TOWARDS AN EDUCATIONAL PLANNING INFORMATION SYSTEM IN BIG DATA ENVIRONMENT -- CNN-based Face Emotion Detection and Mouse Movement Analysis to Detect Student's Engagement Level -- Data Cleaning in Machine Learning : Improving Real Life Decisions and Challenges -- Blockchain-Based Cloud Computing: Model-Driven Engineering Approach -- Student Attention Estimation Based on Body Gesture -- Cat Swarm Optimization Algorithm for DNA Fragment Assembly Problem -- DSGE AND ABM, TOWARDS A "TRUE" REPRESENTATION OF THE REAL WORLD? -- Predictive Hiring System: Information Technology Consultants Soft Skills -- Automated Quality Inspection Using Computer Vision: a Review -- A Comparative Study of Adaptative Learning Algorithms for Students' Performance Prediction: Application in a Moroccan University Computer Science Course -- Pedestrian Orientation Estimation using Deep Learning -- Artificial intelligence application in drought assessment, monitoring and forecasting using available remote sensed data -- CSR communication through social networks: the case of committed brandbanks in Morocco -- Content-Based Image Retrieval Using Octree Quantization Algorithm -- Release Planning Process Model in Agile Global Software Development -- Developing a New Indicator Model to Trade Gold Market -- A new model indicator to trade Foreign Exchange market -- Improving Arabic to English Machine Translation -- Artificial Neural Network with Learning Analytics for Student Performance Prediction in Online Learning Environment -- Attentive Neural Seq2Seq for Arabic Question Generation -- Microservice-Specific Language, a step to the Low-code platforms -- Case Study of Economic Dispatch Problem in Smart Grid System -- Teaching Soft Skills online, what are the most appropriate pedagogical paradigms? -- Road Object Detection: A Case Study of Deep Learning-Based Algorithms -- A comparative review of Tweets Automatic Sarcasm Detection in Arabic and English -- Mobile payment as a lever for financial inclusion -- New Approach to Interconnect Hybride Blockchains -- A Variable Neighborhood Search (VNS) heuristic algorithm based classifier for credit scoring -- Application of Machine Learning Techniques To Enhance Decision Making Lifecycle -- A Smart interactive decision support system for real-time adaptation in the mobility strategy for optimization of the employee's transportation -- A Hesitant fuzzy Holdout method for models' selection in Machine Learning -- Interpretable Credit Scoring Model Via Rule Ensemble -- A New Distributed Architecture Based on Reinforcement Learning for Parameter Estimation in Image Processing -- Smart Sourcing Framework for Public Procurement Announcements Using Machine Learning Models -- An MCDM-Based Methodology for Influential Nodes Detection in a Social Network. Facebook as a Case Study -- A Predictive Approach based on Feature Selection to Improve Email Marketing Campaign Success Rate -- DIAGNOSIS AND ADJUSTMENT FOR SUSTAINABLE TOURISM.

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Weitere Ausg.: Printed edition: ISBN 9783031263835

Weitere Ausg.: Printed edition: ISBN 9783031263859

Sprache: Englisch

DOI: 10.1007/978-3-031-26384-2

URL: https://doi.org/10.1007/978-3-031-26384-2

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Umfang: 1 Online-Ressource (xxix, 871 Seiten) : , 236 Illustrationen, 210 in Farbe.

ISBN: 978-3-031-44696-2

Serie: Lecture notes in computer science 14303

Weitere Ausg.: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-44695-5

Weitere Ausg.: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-44697-9

Sprache: Englisch

Schlagwort(e): Natürliche Sprache ; Sprachverarbeitung ; Wissensverarbeitung ; Information Retrieval ; Maschinelle Übersetzung ; World Wide Web 2.0 ; Chinesisch ; Computerlinguistik ; Konferenzschrift ; Konferenzschrift ; Konferenzschrift ; Konferenzschrift

DOI: 10.1007/978-3-031-44696-2

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