Applying Data Augmentation for Disambiguating Author Names

  • Luciano V. B. Espiridião Instituto Federal de Minas Gerais (IFMG) / Universidade Federal de Ouro Preto (UFOP)
  • Laura L. Dias Universidade Federal de Ouro Preto (UFOP)
  • Anderson A. Ferreira Universidade Federal de Ouro Preto (UFOP)


Author name ambiguity is one of the most challenging issues that can compromise the information quality in a scholarly digital library. For years, researchers have been searched for solutions to solve such a problem. Despite the many methods already proposed, the question remains open. In this study, we address the issue of producing a more accurate disambiguation function by means of applying data augmentation in the set of data training. We also propose a SyGAR-based data augmentation approach and evaluate our proposal on three collections commonly used in works about author name disambiguation task. The experimental results showed scenarios where improvements are possible in the author name disambiguation task. The proposal of data augmentation outperforms other data augmentation approach, as well as improves some machine learning techniques that were not specifically designed for the author name disambiguation task.

Palavras-chave: Author Disambiguation, Data Augmentation, Machine Learning


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ESPIRIDIÃO, Luciano V. B.; DIAS, Laura L.; FERREIRA, Anderson A.. Applying Data Augmentation for Disambiguating Author Names. In: SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 36. , 2021, Rio de Janeiro. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2021 . p. 109-120. ISSN 2763-8979. DOI: