Métodos de inteligencia artificial para la clasificación de documentos en español

Autores/as

  • 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

Palabras clave:

aprendizaje automático, aprendizaje profundo, aumento de datos, clasificación de documentos, inteligencia artificial

Resumen

La rápida globalización y la creciente necesidad de comunicación interlingüística requieren corpus modernos y en tiempo real para ayudar a los estudiantes de idiomas. Los métodos tradicionales para crear dichos corpus, especialmente en español, son inadecuados debido a su incapacidad para procesar la gran cantidad de datos no estructurados disponibles en internet. En este estudio se exploran las metodologías de inteligencia artificial (IA) para la adquisición automática de documentos en español de la web, preprocesándolos y clasificándolos con el fin de construir un corpus vasto y flexible para el aprendizaje del español. La investigación aplica el rastreo web mediante el framework Scrapy para recopilar datos, que luego se limpian y clasifican utilizando modelos avanzados de procesamiento del lenguaje natural (PLN). En concreto, el estudio emplea el algoritmo BERT (Bidirectional Encoder Representations from Transformers) y su variante mejorada RoBERTa para lograr la clasificación de documentos. Mediante una combinación de técnicas de aumento de datos y modelos de aprendizaje profundo, el estudio logra una alta precisión en la clasificación de texto en español, lo que demuestra el potencial del uso de la IA para superar las limitaciones de los enfoques tradicionales de creación de corpus.

Biografía del autor/a

  • 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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Publicado

2024-12-30

Número

Vol. 23 Núm. 2 (2024)

Sección

Dossier sobre inteligencia artificial, lenguaje y discurso digital

Licencia

Derechos de autor 2024 Tad Gonsalves, Hu Hang, Yoshimi Hiroyasu

Creative Commons License

Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.

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Cómo citar

Gonsalves, T., Hang, H., & Hiroyasu, Y. (2024). Métodos de inteligencia artificial para la clasificación de documentos en español. Lengua Y Sociedad, 23(2), 1047-1068. https://doi.org/10.15381/lengsoc.v23i2.29208