@inproceedings{caporusso-etal-2023-ijs,
    title = "{IJS}@{LT}-{EDI} : Ensemble Approaches to Detect Signs of Depression from Social Media Text",
    author = "Caporusso, Jaya  and
      Tran, Thi Hong Hanh  and
      Pollak, Senja",
    editor = "Chakravarthi, Bharathi R.  and
      Bharathi, B.  and
      Griffith, Joephine  and
      Bali, Kalika  and
      Buitelaar, Paul",
    booktitle = "Proceedings of the Third Workshop on Language Technology for Equality, Diversity and Inclusion",
    month = sep,
    year = "2023",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd., Shoumen, Bulgaria",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2023.ltedi-1.26/",
    pages = "172--178",
    abstract = "This paper presents our ensembling solutions for detecting signs of depression in social media text, as part of the Shared Task at LT-EDI@RANLP 2023. By leveraging social media posts in English, the task involves the development of a system to accurately classify them as presenting signs of depression of one of three levels: {\textquotedblleft}severe{\textquotedblright}, {\textquotedblleft}moderate{\textquotedblright}, and {\textquotedblleft}not depressed{\textquotedblright}. We verify the hypothesis that combining contextual information from a language model with local domain-specific features can improve the classifier`s performance. We do so by evaluating: (1) two global classifiers (support vector machine and logistic regression); (2) contextual information from language models; and (3) the ensembling results."
}