Abstract
Depression is a mental illness that negatively affects a person’s well-being and can, if left untreated, lead to serious consequences such as suicide. Therefore, it is important to recognize the signs of depression early. In the last decade, social media has become one of the most common places to express one’s feelings. Hence, there is a possibility of text processing and applying machine learning techniques to detect possible signs of depression. In this paper, we present our approaches to solving the shared task titled Detecting Signs of Depression from Social Media Text. We explore three different approaches to solve the challenge: fine-tuning BERT model, leveraging AutoML for the construction of features and classifier selection and finally, we explore latent spaces derived from the combination of textual and knowledge-based representations. We ranked 9th out of 31 teams in the competition. Our best solution, based on knowledge graph and textual representations, was 4.9% behind the best model in terms of Macro F1, and only 1.9% behind in terms of Recall.- Anthology ID:
- 2022.ltedi-1.36
- 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:
- 251–257
- Language:
- URL:
- https://aclanthology.org/2022.ltedi-1.36
- DOI:
- 10.18653/v1/2022.ltedi-1.36
- Cite (ACL):
- Ilija Tavchioski, Boshko Koloski, Blaž Škrlj, and Senja Pollak. 2022. E8-IJS@LT-EDI-ACL2022 - BERT, AutoML and Knowledge-graph backed Detection of Depression. In Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion, pages 251–257, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- E8-IJS@LT-EDI-ACL2022 - BERT, AutoML and Knowledge-graph backed Detection of Depression (Tavchioski et al., LTEDI 2022)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2022.ltedi-1.36.pdf
- Data
- Wikidata5M