Comparison of Pretraining Models and Strategies for Health-Related Social Media Text Classification.

ABSTRACT: Pretrained contextual language models proposed in the recent past have been reported to achieve state-of-the-art performances in many natural language processing (NLP) tasks, including those involving health-related social media data. We sought to evaluate the effectiveness of different pretrained transformer-based models for social media-based health-related text classification tasks. An additional objective was to explore and propose effective pretraining strategies to improve machine learning performance on such datasets and tasks. We benchmarked six transformer-based models that were pretrained with texts from different domains and sources-BERT, RoBERTa, BERTweet, TwitterBERT, BioClinical_BERT, and BioBERT-on 22 social media-based health-related text classification tasks. For the top-p

SUBMITTER: Guo Y 

PROVIDER: S-EPMC9408372 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature