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: “stressed” or “not stressed.” 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.- Anthology ID:
- 2024.ltedi-1.26
- Volume:
- Proceedings of the Fourth Workshop on Language Technology for Equality, Diversity, Inclusion
- Month:
- March
- Year:
- 2024
- Address:
- St. Julian's, Malta
- Editors:
- Bharathi Raja Chakravarthi, Bharathi B, Paul Buitelaar, Thenmozhi Durairaj, György Kovács, Miguel Ángel García Cumbreras
- Venues:
- LTEDI | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 216–220
- Language:
- URL:
- https://aclanthology.org/2024.ltedi-1.26
- DOI:
- Cite (ACL):
- A Reddy, Ann Thomas, Pranav Moorthi, and Bharathi B. 2024. SSN-Nova@LT-EDI 2024: Leveraging Vectorisation Techniques in an Ensemble Approach for Stress Identification in Low-Resource Languages. In Proceedings of the Fourth Workshop on Language Technology for Equality, Diversity, Inclusion, pages 216–220, St. Julian's, Malta. Association for Computational Linguistics.
- Cite (Informal):
- SSN-Nova@LT-EDI 2024: Leveraging Vectorisation Techniques in an Ensemble Approach for Stress Identification in Low-Resource Languages (Reddy et al., LTEDI-WS 2024)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2024.ltedi-1.26.pdf