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arXiv · 2106.12798

Evaluation of Representation Models for Text Classification with AutoML Tools

Sebastian Brändle·Marc Hanussek·Matthias Blohm·Maximilien Kintz
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Abstract

Automated Machine Learning (AutoML) has gained increasing success on tabular data in recent years. However, processing unstructured data like text is a challenge and not widely supported by open-source AutoML tools. This work compares three manually created text representations and text embeddings automatically created by AutoML tools. Our benchmark includes four popular open-source AutoML tools and eight datasets for text classification purposes. The results show that straightforward text representations perform better than AutoML tools with automatically created text embeddings.

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Sebastian Brändle, Marc Hanussek, Matthias Blohm, Maximilien Kintz. 2021-06-24. Evaluation of Representation Models for Text Classification with AutoML Tools. https://arxiv.org/abs/2106.12798

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