Papers › DUTH at SemEval-2020 Task 11: BERT with Entity Mapping for Propaganda Classification

DUTH at SemEval-2020 Task 11: BERT with Entity Mapping for Propaganda Classification

22 Aug 2020SEMEVAL 2020arXiv:2008.09894archive 2025-07-28

Anastasios Bairaktaris, Symeon Symeonidis, Avi Arampatzis

This report describes the methods employed by the Democritus University of Thrace (DUTH) team for participating in SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articles. Our team dealt with Subtask 2: Technique Classification. We used shallow Natural Language Processing (NLP) preprocessing techniques to reduce the noise in the dataset, feature selection methods, and common supervised machine learning algorithms. Our final model is based on using the BERT system with entity mapping. To improve our model's accuracy, we mapped certain words into five distinct categories by employing word-classes and entity recognition.

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Articles BIG-bench Machine Learning General Classification feature selection

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Adam Attention Attention Dropout BERT Dense Connections Dropout Feature Selection Layer Normalization Linear Layer Linear Warmup With Linear Decay Multi-Head Attention Residual Connection Softmax Weight Decay WordPiece

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