2025
SEMEVAL
SemEval 2025
Tuebingen at SemEval-2025 Task 10: Class Weighting, External Knowledge and Data Augmentation in BERT Models
Abstract
AbstractThe spread of disinformation and propaganda in online news presents a significant challengeto information integrity. As part of the SemEval 2025 Task-10 on Multilingual Characterization and Extraction of Narratives from Online News, this study focuses on Subtask 1: Entity Framing, which involves assigning roles to named entities within news articles across multiple languages.We investigate techniques such as data augmentation, external knowledge, and class weighting to improve classification performance. Our findings indicate that class weighting was more effective than other approaches
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Interdisciplinary Bridge
— Deep Learning and Machine Learning
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio