Publicación:
Spanish Sentiment Analysis Using Universal Language Model Fine-Tuning: A Detailed Case of Study

No hay miniatura disponible
Fecha
2020
Autores
Palomino D.
Ochoa-Luna J.
Título de la revista
Revista ISSN
Título del volumen
Editor
Springer
Proyectos de investigación
Unidades organizativas
Número de la revista
Abstracto
Transfer Learning has emerged as one of the main image classification techniques for reusing architectures and weights trained on big datasets so as to improve small and specific classification tasks. In Natural Language Processing, a similar effect is obtained by reusing and transferring a language model. In particular, the Universal Language Fine-Tuning (ULMFiT) algorithm has proven to have an impressive performance on several English text classification tasks. In this paper, we aim at improving current state-of-the-art algorithms for Spanish Sentiment Analysis of short texts. In order to do so, we have adapted a ULMFiT algorithm to this setting. Experimental results on benchmark datasets show the potential of our approach. © Springer Nature Switzerland AG 2020.
Descripción
Palabras clave
Transfer Learning, Language Model, Natural Language Processing, Sentiment Analysis
Citación