Infotec + CentroGEO at SemEval-2020 Task 8: Deep Learning and Text Categorization approach for Memes classification
Guillermo Ruiz, Eric S. Tellez, Daniela Moctezuma, Sabino Miranda-Jiménez, Tania Ramírez-delReal, Mario Graff
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.- Anthology ID:
- 2020.semeval-1.151
- 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:
- 1141–1147
- Language:
- URL:
- https://aclanthology.org/2020.semeval-1.151
- DOI:
- 10.18653/v1/2020.semeval-1.151
- Cite (ACL):
- Guillermo Ruiz, Eric S. Tellez, Daniela Moctezuma, Sabino Miranda-Jiménez, Tania Ramírez-delReal, and Mario Graff. 2020. Infotec + CentroGEO at SemEval-2020 Task 8: Deep Learning and Text Categorization approach for Memes classification. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 1141–1147, Barcelona (online). International Committee for Computational Linguistics.
- Cite (Informal):
- Infotec + CentroGEO at SemEval-2020 Task 8: Deep Learning and Text Categorization approach for Memes classification (Ruiz et al., SemEval 2020)
- PDF:
- https://preview.aclanthology.org/ml4al-ingestion/2020.semeval-1.151.pdf