Eric Sadit Tellez

Also published as: Eric S. Tellez


2020

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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
Proceedings of the Fourteenth Workshop on Semantic Evaluation

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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INGEOTEC at SemEval-2020 Task 12: Multilingual Classification of Offensive Text
Sabino Miranda-Jiménez | Eric S. Tellez | Mario Graff | Daniela Moctezuma
Proceedings of the Fourteenth Workshop on Semantic Evaluation

This paper describes our participation in OffensEval challenges for English, Arabic, Danish, Turkish, and Greek languages. We used several approaches, such as μTC, TextCategorization, and EvoMSA. Best results were achieved with EvoMSA, which is a multilingual and domain-independent architecture that combines the prediction from different knowledge sources to solve text classification problems.

2018

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INGEOTEC at SemEval-2018 Task 1: EvoMSA and μTC for Sentiment Analysis
Mario Graff | Sabino Miranda-Jiménez | Eric S. Tellez | Daniela Moctezuma
Proceedings of the 12th International Workshop on Semantic Evaluation

This paper describes our participation in Affective Tweets task for emotional intensity and sentiment intensity subtasks for English, Spanish, and Arabic languages. We used two approaches, μTC and EvoMSA. The first one is a generic text categorization and regression system; and the second one, a two-stage architecture for Sentiment Analysis. Both approaches are multilingual and domain independent.

2017

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INGEOTEC at SemEval 2017 Task 4: A B4MSA Ensemble based on Genetic Programming for Twitter Sentiment Analysis
Sabino Miranda-Jiménez | Mario Graff | Eric Sadit Tellez | Daniela Moctezuma
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

This paper describes the system used in SemEval-2017 Task 4 (Subtask A): Message Polarity Classification for both English and Arabic languages. Our proposed system is an ensemble of two layers, the first one uses our generic framework for multilingual polarity classification (B4MSA) and the second layer combines all the decision function values predicted by B4MSA systems using a non-linear function evolved using a Genetic Programming system, EvoDAG. With this approach, the best performances reached by our system were macro-recall 0.68 (English) and 0.477 (Arabic) which set us in sixth and fourth positions in the results table, respectively.