On the use of Query by Committee for Human-in-the-Loop Named Entity Recognition

  • Gabriel Corvino Universidade de Brasília
  • Vitor Vasconcelos Oliveira Universidade de Brasília
  • Angelo C. Mendes da Silva Universidade de São Paulo
  • Ricardo Marcondes Marcacini Universidade de São Paulo

Resumo


Named Entity Recognition is a relevant task for extracting information from textual data. Traditional methods for training NER models assume that humans annotate entities manually, identifying entities in predefined categories. This strategy presents a great human effort, mainly in more specific application domains. To address these challenges, we consider Human in the Loop (HITL), which can be understood as a set of strategies to incorporate human knowledge and experience into machine learning, while accelerating model training. In this paper, we investigate a classic method called Query by Committee (QBC), which helps to select informative instances for data labeling. Traditionally, QBC selects instances with a high level of disagreement between different models of a committee. We present heuristics for QBC relaxation to also consider some level of agreement. We showed that taking advantage of some committee agreement for pre-labeling of instances is promising to speed up human feedback and increase the training set. Experimental results showed that our method is able to preserve the performance of models compared to traditional QBC, while reducing human labeling effort.
Palavras-chave: Human in the Loop, Active Learning, Ensemble, Named Entity Recognition

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Publicado
28/11/2022
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CORVINO, Gabriel; OLIVEIRA, Vitor Vasconcelos; MENDES DA SILVA, Angelo C.; MARCACINI, Ricardo Marcondes. On the use of Query by Committee for Human-in-the-Loop Named Entity Recognition. In: SYMPOSIUM ON KNOWLEDGE DISCOVERY, MINING AND LEARNING (KDMILE), 10. , 2022, Campinas/SP. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2022 . p. 106-113. ISSN 2763-8944. DOI: https://doi.org/10.5753/kdmile.2022.227953.