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A Weakly Supervised Dataset of Fine-Grained Emotions in Portuguese

Domaine:

natural language processing

Type de record:

paperdataset
Créateur:
CorSilCalFre
Hôte:avatar
Affective Computing is the study of how computers can recognize, interpret and simulate human affects. Sentiment Analysis is a common task inNLP related to this topic, but it focuses only on emotion valence (positive, negative, neutral). An emerging approach in NLP is Emotion Recognition, which relies on fined-grained classification. This research describes an approach to create a lexical-based weakly supervised corpus for fine-grained emotion in Portuguese. We evaluated our dataset by fine-tuning a transformer-based language model (BERT) and validating it on a Gold Standard annotated validation set. Our results (F1-score=.64) suggest lexical-based weak supervision as an appropriate strategy for initial work in low resourced environment. Paper published at Symposium in Information and Human Language Technology (STIL 2021)

Visit

arxiv.org

Tasks

emotion identification

Tags

Computation and Language

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