We have released Sina-BERT, a language model pre-trained on BERT (Devlin et al., 2018) to address the lack of a high-quality Persian language model in the medical domain. SINA-BERT utilizes pre-training on a large-scale corpus of medical contents including formal and informal texts collected from a variety of online resources in order to improve the performance on health-care related tasks. We employ SINA-BERT to complete following representative tasks: categorization of medical questions, medical sentiment analysis, and medical question retrieval. For each task, we have developed Persian annotated data sets for training and evaluation and learnt a representation for the data of each task especially complex and long medical questions. With the same architecture being used across tasks, SINA-BERT outperforms BERT-based models that were previously made available in the Persian language.
@article{arxiv.2104.07613,
title = {SINA-BERT: A pre-trained Language Model for Analysis of Medical Texts in Persian},
author = {Nasrin Taghizadeh and Ehsan Doostmohammadi and Elham Seifossadat and Hamid R. Rabiee and Maedeh S. Tahaei},
journal= {arXiv preprint arXiv:2104.07613},
year = {2021}
}