@conference{10272/27977,
year = {2021},
url = {https://hdl.handle.net/10272/27977},
abstract = {In this paper we present our approach and system description on Task 7a in ProfNer-ST: Identification of profession & occupation in Health related Social Media. Our main contribution is to show the effectiveness of using BETO-Spanish BERT for classification tasks in Spanish. In our experiments we compared several architectures based on transformers with others based on classical machine learning algorithms. With this approach, we achieved an F1-score of 0.92 for the positive class in the evaluation process},
publisher = {Association for Computational Linguistics (ACL)},
keywords = {Natural Language Processing},
keywords = {Health-related Social Media},
keywords = {Spanish BERT},
keywords = {Transformer Models},
keywords = {Text Classification},
title = {Identification of profession & occupation in Health-related Social Media using tweets in Spanish},
author = {Pachón Álvarez, Victoria and Mata Vázquez, Jacinto and Domínguez Olmedo, Juan Luis},
}