[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117950-en":3,"doc-seo-117950-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117950,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",6,"Technology","Text Annotation Handbook - A Practical Guide for Machine Learning Projects","A hands-on handbook for approaching text annotation tasks in machine learning projects. It introduces core theoretical concepts while emphasizing practical, readable, and concise guidance, including workflow fundamentals, creation of annotation guidelines, and quality assurance methods such as reviewing and cross-annotation. It also covers acceleration techniques (bootstrapping, weak supervision, active learning), tool selection, and practical refinement steps. Beyond engineering, it addresses business value, impact assessment, and data ethics and regulations, including the Artificial Intelligence Act, bias, and trust-focused guidelines and tools.","Text Annotation Handbook  \nA Practical Guide for Machine Learning Projects  \nThis handbook is a hands-on guide on how to approach text annotation tasks. It provides a gentle introduction to the topic, an overview of theoretical concepts as well as practical advice. The topics covered are mostly technical, but business, ethical and regulatory issues are also touched upon. The focus lies on readability and conciseness rather than completeness and scientific rigor. Experience with annotation and knowledge of machine learning are useful but not required. The document may serve as a primer or reference book for a wide range of professions such as team leaders, project managers, IT architects, software developers and machine learning engineers.  \nMain Editor Felix Stollenwerk (AI Sweden)  \nContributors1  \nFelix Stollenwerk (AI Sweden)  \nJoey Öhman (AI Sweden)  \nDanila Petrelli (AI Sweden) Emma Wallerö ( Ekonomistyrningsverket)  \nFredrik Olsson (Gavagai)  \nCamilla Bengtsson (Gavagai) Andreas Horndahl (Arbetsförmedlingen) Gabriela Zarzar Gandler ( King)  \nAcknowledgements  \nThis work is a result of the “Databeredskapsverkstad” ( Data Readiness Lab) project funded by Vinnova (Sweden’s innovation agency) under grant 2021-03630.  \n1 Contributors have participated in discussions and text reviewing. Authors are specified at the beginning of each section.  \nTable of Contents  \n1 Introduction......................................................................................................................... 3  \n2 Annotation Principle........................................................................................................... 4  \n3 Annotation Basics...............................................................................................................5  \n3. 1 Understanding of the Task............................................................................................5  \n3.2 Creation of Annotation Guidelines................................................................................7  \n3.3 Basic Annotation Workflow...........................................................................................8  \n4 Quality Assurance............................................................................................................. 10  \n4 . 1 Reviewing................................................................................................................... 10  \n4.2 Cross-annotation........................................................................................................ 11  \n5 Acceleration....................................................................................................................... 12  \n5 . 1 Bootstrapping............................................................................................................. 13  \n5.2 Weak Supervision...................................................................................................... 13  \n5 .3 Active Learning........................................................................................................... 14  \n6 Annotation Tools............................................................................................................... 15  \n7 Annotation in Practice...................................................................................................... 16  \n7. 1 Refinement of Guidelines........................................................................................... 16  \n7.2 Class System Adjustments......................................................................................... 17  \n7.3 Representativeness of the Test Dataset..................................................................... 18  \n8 The Business Perspective................................................................................................20  \n8.1 Annotated Data and Business Value.......................................................................... 20  \n8.2 Impact Assessment...","cbCairbgkWUBLS0h","https://ap.wps.com/l/cbCairbgkWUBLS0h","pdf",1115613,1,30,"English","en",105,"# 1 Introduction\n# 2 Annotation Principle\n# 3 Annotation Basics\n## 3.1 Understanding of the Task\n## 3.2 Creation of Annotation Guidelines\n## 3.3 Basic Annotation Workflow\n# 4 Quality Assurance\n## 4.1 Reviewing\n## 4.2 Cross-annotation\n# 5 Acceleration\n## 5.1 Bootstrapping\n## 5.2 Weak Supervision\n## 5.3 Active Learning\n# 6 Annotation Tools\n# 7 Annotation in Practice\n## 7.1 Refinement of Guidelines\n## 7.2 Class System Adjustments\n## 7.3 Representativeness of the Test Dataset\n# 8 The Business Perspective\n## 8.1 Annotated Data and Business Value\n## 8.2 Impact Assessment\n## 8.3 The Value of Annotated Data\n# 9 Ethics and Regulations\n## 9.1 The Artificial Intelligence Act\n## 9.2 Guidelines and Tools for Trust\n## 9.3 Bias and Discrimination\n# Bibliography\n# Appendix","[{\"question\":\"What does annotation mean in this handbook?\",\"answer\":\"Annotation assigns labels to entire documents, sentences, or words. 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