SSN-Nova@LT-EDI 2024: Leveraging Vectorisation Techniques in an Ensemble Approach for Stress Identification in Low-Resource Languages

A Reddy, Ann Thomas, Pranav Moorthi, Bharathi B


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:
Bibkey:
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)
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PDF:
https://aclanthology.org/2024.ltedi-1.26.pdf
Video:
 https://aclanthology.org/2024.ltedi-1.26.mp4