Abstract
Depression is a mental condition related to sadness and the lack of interest in common daily tasks. In this working-notes, we describe the proposal of the UMUTeam in the LT-EDI shared task (ACL 2022) concerning the identification of signs of depression in social network posts. This task is somehow related to other relevant Natural Language Processing tasks such as Emotion Analysis. In this shared task, the organisers challenged the participants to distinguish between moderate and severe signs of depression (or no signs of depression at all) in a set of social posts written in English. Our proposal is based on the combination of linguistic features and several sentence embeddings using a knowledge integration strategy. Our proposal achieved the 6th position, with a macro f1-score of 53.82 in the official leader board.- Anthology ID:
- 2022.ltedi-1.17
- Volume:
- Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Editors:
- Bharathi Raja Chakravarthi, B Bharathi, John P McCrae, Manel Zarrouk, Kalika Bali, Paul Buitelaar
- Venue:
- LTEDI
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 145–148
- Language:
- URL:
- https://aclanthology.org/2022.ltedi-1.17
- DOI:
- 10.18653/v1/2022.ltedi-1.17
- Cite (ACL):
- José García-Díaz and Rafael Valencia-García. 2022. UMUTeam@LT-EDI-ACL2022: Detecting Signs of Depression from text. In Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion, pages 145–148, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- UMUTeam@LT-EDI-ACL2022: Detecting Signs of Depression from text (García-Díaz & Valencia-García, LTEDI 2022)
- PDF:
- https://preview.aclanthology.org/emnlp22-frontmatter/2022.ltedi-1.17.pdf