CIMAT-NLP@LT-EDI-2023: Finegrain Depression Detection by Multiple Binary Problems Approach
María de Jesús García Santiago, Fernando Sánchez Vega, Adrián Pastor López Monroy
Abstract
This work described the work of the team CIMAT-NLP on the Shared task of Detecting Signs of Depression from Social Media Text at LT-EDI@RANLP 2023, which consists of depression classification on three levels: “not depression”, “moderate” depression and “severe” depression on text from social media. In this work, we proposed two approaches: (1) a transformer model which can handle big text without truncation of its length, and (2) an ensemble of six binary Bag of Words. Our team placed fourth in the competition and found that models trained with our approaches could place second- Anthology ID:
- 2023.ltedi-1.18
- Volume:
- Proceedings of the Third Workshop on Language Technology for Equality, Diversity and Inclusion
- Month:
- September
- Year:
- 2023
- Address:
- Varna, Bulgaria
- Editors:
- Bharathi R. Chakravarthi, B. Bharathi, Joephine Griffith, Kalika Bali, Paul Buitelaar
- Venues:
- LTEDI | WS
- SIG:
- Publisher:
- INCOMA Ltd., Shoumen, Bulgaria
- Note:
- Pages:
- 124–130
- Language:
- URL:
- https://aclanthology.org/2023.ltedi-1.18
- DOI:
- Cite (ACL):
- María de Jesús García Santiago, Fernando Sánchez Vega, and Adrián Pastor López Monroy. 2023. CIMAT-NLP@LT-EDI-2023: Finegrain Depression Detection by Multiple Binary Problems Approach. In Proceedings of the Third Workshop on Language Technology for Equality, Diversity and Inclusion, pages 124–130, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.
- Cite (Informal):
- CIMAT-NLP@LT-EDI-2023: Finegrain Depression Detection by Multiple Binary Problems Approach (García Santiago et al., LTEDI-WS 2023)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2023.ltedi-1.18.pdf