Improving Disease Detection from Social Media Text via Self-Augmentation and Contrastive Learning

arXiv:2405.01597v1 Announce Type: new
Abstract: Detecting diseases from social media has diverse applications, such as public health monitoring and disease spread detection. While language models (LMs) have shown promising performance in this domain, there remains ongoing research aimed at refining their discriminating representations. In this paper, we propose a novel method that integrates Contrastive Learning (CL) with language modeling to address this challenge. Our approach introduces a self-augmentation method, wherein hidden representations of the model are augmented with their own …

abstract applications arxiv augmentation cs.cl detection disease diseases disease spread diverse diverse applications domain health improving language language models lms media monitoring paper performance public public health research social social media text type via while