E8-IJS@LT-EDI-ACL2022 - BERT, AutoML and Knowledge-graph backed Detection of Depression

• DOI: 10.18653/v1/2022.ltedi-1.36
• Publication Date: 2022-06-03T01:34:53Z
• Authors: Ilija Tavchioski; Boshko Koloski; Blaž Škrlj; Senja Pollak
• Keywords: Macro; Knowledge graph

Depression is a mental illness that negatively affects person’s well-being and can, if left untreated, lead to serious consequences such as suicide. Therefore, it important recognize the signs of depression early. In last decade, social media has become one most common places express one’s feelings. Hence, there possibility text processing applying machine learning techniques detect possible depression. this paper, we present our approaches solving shared task titled Detecting Signs from Social Media Text. We explore three different solve challenge: fine-tuning BERT model, leveraging AutoML for construction features classifier selection finally, latent spaces derived combination textual knowledge-based representations. ranked 9th out 31 teams in competition. Our best solution, based on knowledge graph representations, was 4.9% behind model terms Macro F1, only 1.9% Recall.