@inproceedings{reddy-etal-2024-ssn,
    title = "{SSN}-Nova@{LT}-{EDI} 2024: Leveraging Vectorisation Techniques in an Ensemble Approach for Stress Identification in Low-Resource Languages",
    author = "Reddy, A  and
      Thomas, Ann  and
      Moorthi, Pranav  and
      B, Bharathi",
    editor = {Chakravarthi, Bharathi Raja  and
      B, Bharathi  and
      Buitelaar, Paul  and
      Durairaj, Thenmozhi  and
      Kov{\'a}cs, Gy{\"o}rgy  and
      Garc{\'i}a Cumbreras, Miguel {\'A}ngel},
    booktitle = "Proceedings of the Fourth Workshop on Language Technology for Equality, Diversity, Inclusion",
    month = mar,
    year = "2024",
    address = "St. Julian's, Malta",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.ltedi-1.26/",
    pages = "216--220",
    abstract = "This paper presents our submission for Shared task on Stress Identification in Dravidian Languages: StressIdent LT-EDI@EACL2024. The objective of this task is to identify stress levels in individuals based on their social media content. The system is tasked with analysing posts written in a code-mixed language of Tamil and Telugu and categorising them into two labels: {\textquotedblleft}stressed{\textquotedblright} or {\textquotedblleft}not stressed.{\textquotedblright} Our approach aimed to leverage feature extraction and juxtapose the performance of widely used traditional, deep learning and transformer models. Our research highlighted that building a pipeline with traditional classifiers proved to significantly improve their performance (0.98 and 0.93 F1-scores in Telugu and Tamil respectively), surpassing the baseline as well as deep learning and transformer models."
}