Artificial Intelligence Methods for Spanish Documents Classification

Authors

  • Tad Gonsalves Sophia University, Tokyo, Japan
  • Hang Hu Sophia University, Tokyo, Japan
  • Yoshimi Hiroyasu Sophia University, Tokyo, Japan https://orcid.org/0000-0001-5596-9933

DOI:

https://doi.org/10.15381/lengsoc.v23i2.29208

Keywords:

artificial intelligence, machine learning, deep learning, data augmentation, document classification

Abstract

The rapid globalization and growing need for cross-language communication necessitate modern, real-time corpora to aid language learners. Traditional methods for creating such corpora, especially in Spanish, are inadequate due to their inability to process the vast and unstructured data available online. This study explores Artificial Intelligence (AI) methodologies for automatic Spanish document acquisition from the web, pre-processing and classifying them in order to build a vast and flexible corpus for Spanish learning. The research applies web crawling using the Scrapy framework to collect data, which is then cleaned and classified using advanced Natural Language Processing (NLP) models. Specifically, the study employs BERT (Bidirectional Encoder Representations from Transformers) and its enhanced variant RoBERTa to achieve document classification. Through a combination of data augmentation techniques and deep learning models, the study achieves high accuracy in classifying Spanish-language texts, demonstrating the potential for using AI to overcome the limitations of traditional corpus-building approaches.

Author Biographies

  • Tad Gonsalves, Sophia University, Tokyo, Japan

    Is a full professor in the Department of Information and Communication Sciences, Faculty of Science and Technology, Sophia University, Tokyo, Japan. His research areas include bio-inspired optimization techniques and the application of deep learning techniques to diverse problems like autonomous driving, drones, digital art and music, and computational linguistics. Of late, he is also developing Affective Computing models. Gonsalves holds a BS in Theoretical Physics and MS in Astrophysics. He earned his PhD in Information Systems from Sophia University, Tokyo, Japan. His research laboratory (https://www.gonken.tokyo/) in Tokyo specializes in applications of deep learning and multi-GPU computing.  Gonsalves has published over a hundred and fifty papers in international conferences and journals. He is the author of the book Artificial Introduction: A Non-Technical Introduction (2017) Sophia University Press, Tokyo, Japan, and co-author of Artificial Intelligence for Business Optimization: Research and Applications (2021), CRC press, London.

  • Hang Hu, Sophia University, Tokyo, Japan

    Obtained the BS in Software Engineering from Beijing University of Posts and Telecommunications, and MS degree in Information Science from Sophia University, Tokyo, Japan. At the under-graduate level, he worked on the development of social network content analysis system with self-designed crawler. He started with crawler based on Scrapy, basic data processing and analysis with various methods, and developed a website with Django for presentation. He also has hands-on experience in developing Mobile and Cloud Applications. His research field is natural language processing, especially the classification of web texts through fine-tuning of pre-trained deep learning models.

  • Yoshimi Hiroyasu, Sophia University, Tokyo, Japan

    Is a full professor at the Center for Language Education and Research (CLER) at Sophia University in Tokyo. She obtained her MA in Linguistics from Sophia University. Since 1989, she has been engaged in the field of teaching Spanish as a Foreign Language (ELE). Her publications include several grammar books, self-study Spanish books, and dictionaries. She has also collaborated on numerous Spanish textbooks, notably El español y yo (2013) and ¡Muy bien! 1 and 2 (2018 and 2019). Currently, she is studying the textual traditions of Spanish teaching in Japan and is developing a corpus of textbooks used in Japan from 1900 to the present.

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Published

2024-12-30

Issue

Vol. 23 No. 2 (2024)

Section

Dossier sobre inteligencia artificial, lenguaje y discurso digital

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Copyright (c) 2024 Tad Gonsalves, Hu Hang, Yoshimi Hiroyasu

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How to Cite

Gonsalves, T., Hang, H., & Hiroyasu, Y. (2024). Artificial Intelligence Methods for Spanish Documents Classification. Lengua Y Sociedad, 23(2), 1047-1068. https://doi.org/10.15381/lengsoc.v23i2.29208