Abstract
This paper describes the Unibuc Archaeology team work for CLPsych’s 2024 Shared Task that involved finding evidence within the text supporting the assigned suicide risk level. Two types of evidence were required: highlights (extracting relevant spans within the text) and summaries (aggregating evidence into a synthesis). Our work focuses on evaluating Large Language Models (LLM) as opposed to an alternative method that is much more memory and resource efficient. The first approach employs an LLM that is used for generating the summaries and is guided to provide sequences of text indicating suicidal tendencies through a processing chain for highlights. The second approach involves implementing a good old-fashioned machine learning tf-idf with a logistic regression classifier, whose representative features we use to extract relevant highlights.- Anthology ID:
- 2024.clpsych-1.25
- Volume:
- Proceedings of the 9th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2024)
- Month:
- March
- Year:
- 2024
- Address:
- St. Julians, Malta
- Editors:
- Andrew Yates, Bart Desmet, Emily Prud’hommeaux, Ayah Zirikly, Steven Bedrick, Sean MacAvaney, Kfir Bar, Molly Ireland, Yaakov Ophir
- Venues:
- CLPsych | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 256–263
- Language:
- URL:
- https://aclanthology.org/2024.clpsych-1.25
- DOI:
- Cite (ACL):
- Anastasia Sandu, Teodor Mihailescu, and Sergiu Nisioi. 2024. Cheap Ways of Extracting Clinical Markers from Texts. In Proceedings of the 9th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2024), pages 256–263, St. Julians, Malta. Association for Computational Linguistics.
- Cite (Informal):
- Cheap Ways of Extracting Clinical Markers from Texts (Sandu et al., CLPsych-WS 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.clpsych-1.25.pdf