Investigation of Deep Active Self-learning Algorithms Applied to Named Entity Recognition

  • José Reinaldo Cunha Santos A. V. Silva Neto Osaka University
  • Thiago de Paulo Faleiros UnB


Active Self-Learning algorithms reduce the labeled data required to train a Machine Learning model through supervised training. This paper explores various Active Self-Learning algorithms for named entity recognition tasks. Firstly, we investigate the impact of different self-training techniques on Active Self-Learning algorithms. Secondly, we propose a novel token-level Active Self-Learning algorithm that achieves near-peak performance using fewer hand-annotated tokens compared to existing works. Through numerous experiments, we found that the sentence-level Active Self-Learning algorithm did not consistently yield significant results compared to pure active learning. However, our proposed token-level Active Self-Learning algorithm showed promising performance, training a neural model to nearly peak accuracy with fewer human-annotated tokens compared to state-of-the-art active learning baseline algorithms. The experimental results are presented and discussed, demonstrating the superior performance of the token-level Active Self-Learning algorithm
SILVA NETO, José Reinaldo Cunha Santos A. V.; FALEIROS, Thiago de Paulo. Investigation of Deep Active Self-learning Algorithms Applied to Named Entity Recognition. In: BRAZILIAN CONFERENCE ON INTELLIGENT SYSTEMS (BRACIS), 12. , 2023, Belo Horizonte/MG. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 470-484. ISSN 2643-6264.