@inproceedings{ermurachi-gifu-2020-uaic1860,
title = "{UAIC}1860 at {S}em{E}val-2020 Task 11: Detection of Propaganda Techniques in News Articles",
author = "Ermurachi, Vlad and
Gifu, Daniela",
editor = "Herbelot, Aurelie and
Zhu, Xiaodan and
Palmer, Alexis and
Schneider, Nathan and
May, Jonathan and
Shutova, Ekaterina",
booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
month = dec,
year = "2020",
address = "Barcelona (online)",
publisher = "International Committee for Computational Linguistics",
url = "https://aclanthology.org/2020.semeval-1.241",
doi = "10.18653/v1/2020.semeval-1.241",
pages = "1835--1840",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T UAIC1860 at SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articles
%A Ermurachi, Vlad
%A Gifu, Daniela
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y May, Jonathan
%Y Shutova, Ekaterina
%S Proceedings of the Fourteenth Workshop on Semantic Evaluation
%D 2020
%8 December
%I International Committee for Computational Linguistics
%C Barcelona (online)
%F ermurachi-gifu-2020-uaic1860
%X 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.
%R 10.18653/v1/2020.semeval-1.241
%U https://aclanthology.org/2020.semeval-1.241
%U https://doi.org/10.18653/v1/2020.semeval-1.241
%P 1835-1840
Markdown (Informal)
[UAIC1860 at SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articles](https://aclanthology.org/2020.semeval-1.241) (Ermurachi & Gifu, SemEval 2020)
ACL