Abstract
The “Detection of Propaganda Techniques in News Articles” task at the SemEval 2020 competition focuses on detecting and classifying propaganda, pervasive in news article. In this paper, we present a system able to evaluate on sentence level, three traditional text representation techniques for these study goals, using: tf*idf, word and character n-grams. Firstly, we built a binary classifier able to provide corresponding propaganda labels, propaganda or non-propaganda. Secondly, we build a multilabel multiclass model to identify applied propaganda.- Anthology ID:
- 2020.semeval-1.241
- Volume:
- Proceedings of the Fourteenth Workshop on Semantic Evaluation
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona (online)
- Editors:
- Aurelie Herbelot, Xiaodan Zhu, Alexis Palmer, Nathan Schneider, Jonathan May, Ekaterina Shutova
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- International Committee for Computational Linguistics
- Note:
- Pages:
- 1835–1840
- Language:
- URL:
- https://aclanthology.org/2020.semeval-1.241
- DOI:
- 10.18653/v1/2020.semeval-1.241
- Cite (ACL):
- Vlad Ermurachi and Daniela Gifu. 2020. UAIC1860 at SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articles. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 1835–1840, Barcelona (online). International Committee for Computational Linguistics.
- Cite (Informal):
- UAIC1860 at SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articles (Ermurachi & Gifu, SemEval 2020)
- PDF:
- https://preview.aclanthology.org/bionlp-24-ingestion/2020.semeval-1.241.pdf