Evaluation of Representation Models for Text Classification with AutoML Tools
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 23 references that are in the map. First posted on arXiv 24 Jun 2021.
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References · 14
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