Text Data Augmentations: Permutation, Antonyms and Negation
(2021)
Journal Article
Haralabopoulos, G., Torres, M. T., Anagnostopoulos, I., & McAuley, D. (2021). Text Data Augmentations: Permutation, Antonyms and Negation. Expert Systems with Applications, 177, Article 114769. https://doi.org/10.1016/j.eswa.2021.114769
Text has traditionally been used to train automated classifiers for a multitude of purposes, such as: classification, topic modelling and sentiment analysis. State-of-the-art LSTM classifier require a large number of training examples to avoid biases... Read More about Text Data Augmentations: Permutation, Antonyms and Negation.