Automatic Identification of Equivalence of Concepts in Different Languages for Never-Ending Learning

  • Silvio C. Marino UFSCAR
  • Estevam R. Hruschka Junior UFSCAR


This paper describes the process of automatic identification of concepts in different languages using a base that relies on simple semantic and morphosyntactic characteristics like string similarity, difference in words amount and translation position on dictionary (when exists) and a neural network that has been used as a model of machine learning. All experiments use data that was obtained from a few categories of Read The Web (RTW) project and an endless learning computation system called NELL: Never-Ending Language Learning. The results were compared with dictionary and showed that the introduction of neural network brought a significant gain in the process of equivalence of concepts.


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MARINO, Silvio C.; HRUSCHKA JUNIOR, Estevam R.. Automatic Identification of Equivalence of Concepts in Different Languages for Never-Ending Learning. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 15. , 2018, São Paulo. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2018 . p. 150-161. ISSN 2763-9061. DOI: