Improving short text classification through global augmentation methods
V Marivate, T Sefara - Machine Learning and Knowledge Extraction: 4th …, 2020 - Springer
Machine Learning and Knowledge Extraction: 4th IFIP TC 5, TC 12, WG 8.4, WG 8 …, 2020•Springer
We study the effect of different approaches to text augmentation. To do this we use three
datasets that include social media and formal text in the form of news articles. Our goal is to
provide insights for practitioners and researchers on making choices for augmentation for
classification use cases. We observe that Word2Vec-based augmentation is a viable option
when one does not have access to a formal synonym model (like WordNet-based
augmentation). The use of mixup further improves performance of all text based …
datasets that include social media and formal text in the form of news articles. Our goal is to
provide insights for practitioners and researchers on making choices for augmentation for
classification use cases. We observe that Word2Vec-based augmentation is a viable option
when one does not have access to a formal synonym model (like WordNet-based
augmentation). The use of mixup further improves performance of all text based …
Abstract
We study the effect of different approaches to text augmentation. To do this we use three datasets that include social media and formal text in the form of news articles. Our goal is to provide insights for practitioners and researchers on making choices for augmentation for classification use cases. We observe that Word2Vec-based augmentation is a viable option when one does not have access to a formal synonym model (like WordNet-based augmentation). The use of mixup further improves performance of all text based augmentations and reduces the effects of overfitting on a tested deep learning model. Round-trip translation with a translation service proves to be harder to use due to cost and as such is less accessible for both normal and low resource use-cases.
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