@inproceedings{ruiz-etal-2020-infotec,
title = "Infotec + {C}entro{GEO} at {S}em{E}val-2020 Task 8: Deep Learning and Text Categorization approach for Memes classification",
author = "Ruiz, Guillermo and
Tellez, Eric S. and
Moctezuma, Daniela and
Miranda-Jim{\'e}nez, Sabino and
Ram{\'\i}rez-delReal, Tania and
Graff, Mario",
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-1.151",
doi = "10.18653/v1/2020.semeval-1.151",
pages = "1141--1147",
abstract = "The information shared on social media is increasingly important; both images and text, and maybe the most popular combination of these two kinds of data are the memes. This manuscript describes our participation in Memotion task at SemEval 2020. This task is about to classify the memes in several categories related to the emotional content of them. For the proposed system construction, we used different strategies, and the best ones were based on deep neural networks and a text categorization algorithm. We obtained results analyzing the text and images separately, and also in combination. Our better performance was achieved in Task A, related to polarity classification.",
}
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<abstract>The information shared on social media is increasingly important; both images and text, and maybe the most popular combination of these two kinds of data are the memes. This manuscript describes our participation in Memotion task at SemEval 2020. This task is about to classify the memes in several categories related to the emotional content of them. For the proposed system construction, we used different strategies, and the best ones were based on deep neural networks and a text categorization algorithm. We obtained results analyzing the text and images separately, and also in combination. Our better performance was achieved in Task A, related to polarity classification.</abstract>
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%0 Conference Proceedings
%T Infotec + CentroGEO at SemEval-2020 Task 8: Deep Learning and Text Categorization approach for Memes classification
%A Ruiz, Guillermo
%A Tellez, Eric S.
%A Moctezuma, Daniela
%A Miranda-Jiménez, Sabino
%A Ramírez-delReal, Tania
%A Graff, Mario
%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 ruiz-etal-2020-infotec
%X The information shared on social media is increasingly important; both images and text, and maybe the most popular combination of these two kinds of data are the memes. This manuscript describes our participation in Memotion task at SemEval 2020. This task is about to classify the memes in several categories related to the emotional content of them. For the proposed system construction, we used different strategies, and the best ones were based on deep neural networks and a text categorization algorithm. We obtained results analyzing the text and images separately, and also in combination. Our better performance was achieved in Task A, related to polarity classification.
%R 10.18653/v1/2020.semeval-1.151
%U https://aclanthology.org/2020.semeval-1.151
%U https://doi.org/10.18653/v1/2020.semeval-1.151
%P 1141-1147
Markdown (Informal)
[Infotec + CentroGEO at SemEval-2020 Task 8: Deep Learning and Text Categorization approach for Memes classification](https://aclanthology.org/2020.semeval-1.151) (Ruiz et al., SemEval 2020)
ACL