Learning from multi-annotator data: A noise-aware classification framework

Keyphrases

• Labeled Data 100%
• Classification Framework 100%
• Noisy Labels 100%
• Label Aggregation 100%
• Multi-annotator Data 100%
• Noise-aware 100%
• Training Data 66%
• Simulation Experiment 66%
• Crowdsourcing Platform 66%
• Parameter Selection 33%
• Social Media 33%
• Noisy Data 33%
• Noisy Environment 33%
• Affordable Cost 33%
• Classification Algorithms 33%
• Supervised Learning 33%
• Binary Classification 33%
• Social Background 33%
• Refinement Algorithm 33%
• Text Classification 33%
• Twitter 33%
• Sentiment Analysis 33%
• Social Media Text 33%
• Crowdsourcing 33%
• Background Knowledge 33%
• Social Platform 33%
• Aggregation Performance 33%
• Online Crowdsourcing 33%
• Emotion Detection 33%
• Feedback Strategies 33%
• Annotator 33%
• Noisy Annotations 33%
• Multiple Classification Tasks 33%
• Label Classification 33%
• Ranking Task 33%
• Real Noise 33%
• Huge Amounts of Data 33%
• Textual Form 33%

Computer Science

• Classification Framework 100%
• Sentiment Analysis 33%
• Annotation 33%
• Supervised Learning 33%
• Emotion Detection 33%
• Crowdsourcing Platform 33%
• Background Knowledge 33%
• Training Data 33%
• Simulation Experiment 33%
• Training Dataset 33%
• Text Classification 33%
• Classification Task 33%
• Noisy Environment 33%
• Classification Algorithm 33%
• Social Network 33%