@inproceedings{milintsevich-etal-2024-evaluating,
    title = "Evaluating Lexicon Incorporation for Depression Symptom Estimation",
    author = {Milintsevich, Kirill  and
      Dias, Ga{\"e}l  and
      Sirts, Kairit},
    editor = "Naumann, Tristan  and
      Ben Abacha, Asma  and
      Bethard, Steven  and
      Roberts, Kirk  and
      Bitterman, Danielle",
    booktitle = "Proceedings of the 6th Clinical Natural Language Processing Workshop",
    month = jun,
    year = "2024",
    address = "Mexico City, Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.clinicalnlp-1.28/",
    doi = "10.18653/v1/2024.clinicalnlp-1.28",
    pages = "322--328",
    abstract = "This paper explores the impact of incorporating sentiment, emotion, and domain-specific lexicons into a transformer-based model for depression symptom estimation. Lexicon information is added by marking the words in the input transcripts of patient-therapist conversations as well as in social media posts. Overall results show that the introduction of external knowledge within pre-trained language models can be beneficial for prediction performance, while different lexicons show distinct behaviours depending on the targeted task. Additionally, new state-of-the-art results are obtained for the estimation of depression level over patient-therapist interviews."
}