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Computational methods for depression detection aim to mine traces ofdepression from online publications posted by Internet users. However,solutions trained on existing collections exhibit limited generalisation andinterpretability. To tackle these issues, recent studies have shown thatidentifying depressive symptoms can lead to more robust models. The eRiskinitiative fosters research on this area and has recently proposed a newranking task focused on developing search methods to find sentences related todepressive symptoms. This search challenge relies on the symptoms specified bythe Beck Depression Inventory-II (BDI-II), a questionnaire widely used inclinical practice. Based on the participant systems' results, we present theDepreSym dataset, consisting of 21580 sentences annotated according to theirrelevance to the 21 BDI-II symptoms. The labelled sentences come from a pool ofdiverse ranking methods, and the final dataset serves as a valuable resourcefor advancing the development of models that incorporate depressive markerssuch as clinical symptoms. Due to the complex nature of this relevanceannotation, we designed a robust assessment methodology carried out by threeexpert assessors (including an expert psychologist). Additionally, we explorehere the feasibility of employing recent Large Language Models (ChatGPT andGPT4) as potential assessors in this complex task. We undertake a comprehensiveexamination of their performance, determine their main limitations and analyzetheir role as a complement or replacement for human annotators.

DepreSym: A Depression Symptom Annotated Corpus and the Role of LLMs as Assessors of Psychological Markers