Marky: A tool supporting annotation consistency in multi-user and iterative document annotation projects
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Summary
Marky is a new Web-based document annotation tool equipped to manage multi-user and iterative projects, and to evaluate annotation quality throughout the project life cycle, providing unique means to minimise annotation effort and enforce annotation quality, and therefore corpus consistency.
- Type
- article
- Published
- 2015-02-01
- Cited by
- 26
- References
- 21
- Access
- Open access
- OpenAlex
- https://openalex.org/W2120824164
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:164959
Keywords
Annotation, Computer science, Consistency (knowledge bases), Ajax, Information retrieval
References
- Technical Brief: Agreement, the F-Measure, and Reliability in Information Retrieval
- Inter-annotator Agreement for a German Newspaper Corpus
- Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications
- PubTator: a web-based text mining tool for assisting biocuration
- CAROTID - A web-based platform for optimal personalized management of atherosclerotic patients
- BiOSS: A system for biomedical ontology selection
- Text-mining solutions for biomedical research: enabling integrative biology
- A new tool for sharing and querying of clinical documents modeled using HL7 Version 3 standard
- The EU-ADR corpus: Annotated drugs, diseases, targets, and their relationships
- Collaborative text-annotation resource for disease-centered relation extraction from biomedical text
- A survey on annotation tools for the biomedical literature
- Argo: an integrative, interactive, text mining-based workbench supporting curation
- Construction of an annotated corpus to support biomedical information extraction
- BioAnnote: A software platform for annotating biomedical documents with application in medical learning environments
- Semantic annotation of biological concepts interplaying microbial cellular responses
- MyMiner: a web application for computer-assisted biocuration and text annotation
- Egas: a collaborative and interactive document curation platform
- NCBI Disease Corpus: A Resource for Disease Name Recognition and Concept Normalization
- Agreement
- Noname manuscript No. (will be inserted by the editor) GATE Teamware: A Web-based, Collaborative Text Annotation Framework
Cited by
- Requirements analysis for Dynamic Updating of E-Book Content from User Feedback
- The Markyt visualisation, prediction and benchmark platform for chemical and gene entity recognition at BioCreative/CHEMDNER challenge
- Identifying and reducing inappropriate use of medications using Electronic Health Records
- Construction of antimicrobial peptide-drug combination networks from scientific literature based on a semi-automated curation workflow
- Active Learning and Visual Analytics for Stance Classification with ALVA
- The Feedback Block Model for an Adaptive E-Book
- Collaborative relation annotation and quality analysis in Markyt environment
- Design of User Feedback Interface for Dynamic Updating of E-Book Content
- Sentiment and Stance Visualization of Textual Data for Social Media
- Desarrollo de herramientas inteligentes para vistas especializadas e integradas de la bibliografía clínica
- Manual semantic annotations: User evaluation of interface and interaction designs
- Garbage in, garbage out?: do machine learning application papers in social computing report where human-labeled training data comes from?
- An extensive review of tools for manual annotation of documents
- Study of Annotation Systems in E-business: Classification and Observation
- TeamTat: a collaborative text annotation tool
- Comparison of classification model and annotation method for Undiksha’s official documents
- Study of Healthcare Annotation Systems
- Interactive E-Textbook Platform Based on Block Editing Model in Crowdsourcing E-Learning Environments
- Offensive, aggressive, and hate speech analysis: From data-centric to human-centered approach
- “Garbage in, garbage out” revisited: What do machine learning application papers report about human-labeled training data?
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