@inproceedings{bairaktaris-etal-2020-duth,
title = "{DUTH} at {S}em{E}val-2020 Task 11: {BERT} with Entity Mapping for Propaganda Classification",
author = "Bairaktaris, Anastasios and
Symeonidis, Symeon and
Arampatzis, Avi",
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-1.227",
doi = "10.18653/v1/2020.semeval-1.227",
pages = "1732--1738",
abstract = "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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<abstract>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</abstract>
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%0 Conference Proceedings
%T DUTH at SemEval-2020 Task 11: BERT with Entity Mapping for Propaganda Classification
%A Bairaktaris, Anastasios
%A Symeonidis, Symeon
%A Arampatzis, Avi
%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 bairaktaris-etal-2020-duth
%X 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
%R 10.18653/v1/2020.semeval-1.227
%U https://aclanthology.org/2020.semeval-1.227
%U https://doi.org/10.18653/v1/2020.semeval-1.227
%P 1732-1738
Markdown (Informal)
[DUTH at SemEval-2020 Task 11: BERT with Entity Mapping for Propaganda Classification](https://aclanthology.org/2020.semeval-1.227) (Bairaktaris et al., SemEval 2020)
ACL