Depression is a global health concern with severe consequences for individuals, making its recognition and
understanding crucial. Recently, there has been a growing interest in utilizing social media platforms as
valuable sources of information to gain insights into individuals’ experiences with depression. Analyzing
textual data from diverse user populations enables the identification of common symptoms, triggers,
coping mechanisms, and potential warning signs. Researchers have developed algorithms and machine
learning models to automate the detection of depressive symptoms in text, facilitating more efficient
screening and early intervention. This paper describes the participation of team NailP in the CLEF
eRisk 2023 task 1, which focuses on ranking sentences from user writings based on their relevance to
symptoms of depression. The goal is to evaluate the sentences and determine their level of relevance to
each symptom outlined in the Beck Depression Questionnaire-II. Such participation contributes to the
development of effective methods and tools for identifying and predicting potential risks and dangers
associated with depression in online environments.The authors thank CNPq, CAPES, FAPERJ, and CEFET/RJ for partially funding this research.
The authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for
financial support through national funds FCT/MCTES (PIDDAC) to CeDRI (UIDB/05757/2020
and UIDP/05757/2020) and SusTEC (LA/P/0007/2021).info:eu-repo/semantics/publishedVersio
