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Related Experiment Video

Updated: Oct 14, 2025

Decoding Natural Behavior from Neuroethological Embedding
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Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

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autoBOT: evolving neuro-symbolic representations for explainable low resource text classification.

Blaž Škrlj1,2, Matej Martinc1,2, Nada Lavrač1,3

  • 1Jožef Stefan Institute, Jamova 39, 1000 Ljubljana, Slovenia.

Machine Learning
|November 1, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces autoBOT, an automated machine learning approach that evolves text representations for low-resource scenarios. autoBOT achieves competitive performance in text classification while offering explainability and reduced data requirements.

Keywords:
AutoMLNatural language processingNeuro-symbolic computingRepresentation learning

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Area of Science:

  • Natural Language Processing
  • Machine Learning
  • Artificial Intelligence

Background:

  • State-of-the-art neural language models excel at text classification but are resource-intensive.
  • Training large models requires substantial data, computational power, and extensive hyperparameter tuning.
  • Existing methods pose challenges for low-resource learning environments with limited data and hardware.

Purpose of the Study:

  • To develop an explainable, low-resource text classification method using automatically evolved representations.
  • To present autoBOT (automatic Bags-Of-Tokens), an automated machine learning (autoML) approach for resource-constrained scenarios.
  • To enable competitive performance without the need for massive datasets or extensive computational resources.

Main Methods:

  • An evolutionary algorithm jointly optimizes sparse text representations (word, subword, POS tags, keywords, knowledge graphs, relational features) and document embeddings.
  • Evolution is performed at the representation level, not the learner level.
  • The approach is designed for automated hyperparameter tuning in low-resource settings.

Main Results:

  • autoBOT demonstrates competitive text classification performance across fourteen real-world tasks.
  • The method achieves comparable or superior results to state-of-the-art models like BERT and RoBERTa.
  • Performance is competitive against another autoML approach that evolves ensemble models.

Conclusions:

  • autoBOT provides an effective and explainable solution for text classification in low-resource scenarios.
  • The method reduces the dependency on large datasets and extensive computational resources.
  • The explainability of autoBOT, stemming from input space importance, offers potential for meta-transfer learning.