Nádia Silva


2020

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Deep Learning Brasil - NLP at SemEval-2020 Task 9: Sentiment Analysis of Code-Mixed Tweets Using Ensemble of Language Models
Manoel Veríssimo dos Santos Neto | Ayrton Amaral | Nádia Silva | Anderson da Silva Soares
Proceedings of the Fourteenth Workshop on Semantic Evaluation

In this paper, we describe a methodology to predict sentiment in code-mixed tweets (hindi-english). Our team called verissimo.manoel in CodaLab developed an approach based on an ensemble of four models (MultiFiT, BERT, ALBERT, and XLNET). The final classification algorithm was an ensemble of some predictions of all softmax values from these four models. This architecture was used and evaluated in the context of the SemEval 2020 challenge (task 9), and our system got 72.7% on the F1 score.

2019

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INF-HatEval at SemEval-2019 Task 5: Convolutional Neural Networks for Hate Speech Detection Against Women and Immigrants on Twitter
Alison Ribeiro | Nádia Silva
Proceedings of the 13th International Workshop on Semantic Evaluation

In this paper, we describe our approach to detect hate speech against women and immigrants on Twitter in a multilingual context, English and Spanish. This challenge was proposed by the SemEval-2019 Task 5, where participants should develop models for hate speech detection, a two-class classification where systems have to predict whether a tweet in English or in Spanish with a given target (women or immigrants) is hateful or not hateful (Task A), and whether the hate speech is directed at a specific person or a group of individuals (Task B). For this, we implemented a Convolutional Neural Networks (CNN) using pre-trained word embeddings (GloVe and FastText) with 300 dimensions. Our proposed model obtained in Task A 0.488 and 0.696 F1-score for English and Spanish, respectively. For Task B, the CNN obtained 0.297 and 0.430 EMR for English and Spanish, respectively.

2018

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#TeamINF at SemEval-2018 Task 2: Emoji Prediction in Tweets
Alison Ribeiro | Nádia Silva
Proceedings of the 12th International Workshop on Semantic Evaluation

In this paper, we describe a methodology to predict emoji in tweets. Our approach is based on the classic bag-of-words model in conjunction with word embeddings. The used classification algorithm was Logistic Regression. This architecture was used and evaluated in the context of the SemEval 2018 challenge (task 2, subtask 1).

2014

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Biocom Usp: Tweet Sentiment Analysis with Adaptive Boosting Ensemble
Nádia Silva | Estevam Hruschka | Eduardo Hruschka
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

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Biocom Usp: Tweet Sentiment Analysis with Adaptive Boosting Ensemble
Nádia Silva | Estevam Hruschka | Eduardo Hruschka
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)