Leveraging Automated Machine Learning for Text Classification: Evaluation of AutoML Tools and Comparison with Human Performance
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Abstract (arXiv)
Recently, Automated Machine Learning (AutoML) has registered increasing success with respect to tabular data. However, the question arises whether AutoML can also be applied effectively to text classification tasks. This work compares four AutoML tools on 13 different popular datasets, including Kaggle competitions, and opposes human performance. The results show that the AutoML tools perform better than the machine learning community in 4 out of 13 tasks and that two stand out.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 7 Dec 2020.
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Citing papers per year: 2021: 3; 2022: 1.
4 papers.
- 2021Benchmarking Multimodal AutoML for Tabular Data with Text Fieldscited by 26
- 2021SentimentArcs: A Novel Method for Self-Supervised Sentiment Analysis of Time Series Shows SOTA Transformers Can Struggle Finding Narrative Arcscited by 4
- 2022A Robust Stacking Framework for Training Deep Graph Models with Multifaceted Node Featurescited by 1
- 2021Evaluation of Representation Models for Text Classification with AutoML Toolscited by 0
- 2022A Robust Stacking Framework for Training Deep Graph Models with Multifaceted Node Features16 Jun 2022
- 2021Benchmarking Multimodal AutoML for Tabular Data with Text Fields4 Nov 2021
- 2021SentimentArcs: A Novel Method for Self-Supervised Sentiment Analysis of Time Series Shows SOTA Transformers Can Struggle Finding Narrative Arcs18 Oct 2021
- 2021Evaluation of Representation Models for Text Classification with AutoML Tools24 Jun 2021