@inproceedings{sandu-etal-2024-cheap,
title = "Cheap Ways of Extracting Clinical Markers from Texts",
author = "Sandu, Anastasia and
Mihailescu, Teodor and
Nisioi, Sergiu",
editor = "Yates, Andrew and
Desmet, Bart and
Prud{'}hommeaux, Emily and
Zirikly, Ayah and
Bedrick, Steven and
MacAvaney, Sean and
Bar, Kfir and
Ireland, Molly and
Ophir, Yaakov",
booktitle = "Proceedings of the 9th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2024)",
month = mar,
year = "2024",
address = "St. Julians, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.clpsych-1.25",
pages = "256--263",
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.",
}
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%0 Conference Proceedings
%T Cheap Ways of Extracting Clinical Markers from Texts
%A Sandu, Anastasia
%A Mihailescu, Teodor
%A Nisioi, Sergiu
%Y Yates, Andrew
%Y Desmet, Bart
%Y Prud’hommeaux, Emily
%Y Zirikly, Ayah
%Y Bedrick, Steven
%Y MacAvaney, Sean
%Y Bar, Kfir
%Y Ireland, Molly
%Y Ophir, Yaakov
%S Proceedings of the 9th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2024)
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julians, Malta
%F sandu-etal-2024-cheap
%X 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.
%U https://aclanthology.org/2024.clpsych-1.25
%P 256-263
Markdown (Informal)
[Cheap Ways of Extracting Clinical Markers from Texts](https://aclanthology.org/2024.clpsych-1.25) (Sandu et al., CLPsych-WS 2024)
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.