BioReddit: Word Embeddings for User-Generated Biomedical NLP

Abstract

Word embeddings, in their different shapes and evolutions, have changed the natural language processing research landscape in the last years. The biomedical text processing field is no stranger to this revolution; however, researchers in the field largely trained their embeddings on scientific documents, even when working on user-generated data. In this paper we show how training embeddings from a corpus collected from user-generated text from medical forums heavily influences the performance on downstream tasks, outperforming embeddings trained both on general purpose data or on scientific papers when applied to user-generated content.