Human vs. Automated data annotation: Labeling the data set for an ML-driven support ticket classifier

2026/03/01 by Simon Fuchs, Janik Schnellbach, Holger Wittges +1 · 3 citations

paper · doi:10.1016/j.datak.2025.102534

published in Data & Knowledge Engineering 162, 102534 (Elsevier BV)

crossref created 2025/11/23 · crossref deposited 2026/02/02 · crossref issued 2026/03/01 · crossref published 2026/03/01 · crossref published-print 2026/03/01 · crossref indexed 2026/08/01

Citations

• Bridging the Communication Gap: Evaluating AI Labeling Practices for Trustworthy AI Development
• Using Imperfect Surrogates for Downstream Inference: Design-based Supervised Learning for Social Science Applications of Large Language Models
• Hurdles to Artificial Intelligence Deployment: Noise in Schemas and “Gold” Labels
• Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspective
• Data collection and quality challenges in deep learning: a data-centric AI perspective
• Machine Learning: Algorithms, Real-World Applications and Research Directions
• A Survey of Deep Active Learning
• Active Learning Approaches for Labeling Text: Review and Assessment of the Performance of Active Learning Approaches
• Consistency-based Semi-supervised Active Learning: Towards Minimizing Labeling Cost
• Improving IT Support by Enhancing Incident Management Process with Multi-modal Analysis
• Customer Support Ticket Escalation Prediction using Feature Engineering
• Snorkel: Rapid Training Data Creation with Weak Supervision