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Thoma, M. (2018) The WiLI-2018 Benchmark Dataset for Written Language Iden-tification. arXiv: 1801.07779.

has been cited by the following article:

• TITLE: Comparative Analysis of Neural Networks and Naive Bayes for Multilingual Text Identification

  AUTHORS: Xinyu Zhang, Chaoya Yan, Jiaqing Shen

  KEYWORDS: Language Identification, Character-Level Neural Networks, Naive Bayes, Multilingual Text Classification, CNN/RNN, Character N-Grams, Computational Efficiency, Performance Analysis, Text Processing, Machine Learning, Natural Language Processing, Comparative Study

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.18 No.11, November 27, 2025

  ABSTRACT: This study presents a comparative analysis of two distinct machine learning approaches for multilingual text identification: character-level neural networks (CNN/RNN) and traditional Naive Bayes classifiers. We constructed a dataset comprising 20 languages, including Arabic, Bulgarian, German, Greek, English, Spanish, French, Hindi, Italian, Japanese, Korean, Dutch, Polish, Portuguese, Russian, Swahili, Thai, Turkish, Urdu, and Vietnamese. Experimental results demonstrate that the character-level neural network model achieved 98.76.