@inproceedings{karacan-etal-2024-towards-comprehensive,
title = "Towards Comprehensive Language Analysis for Clinically Enriched Spontaneous Dialogue",
author = "Karacan, Baris and
Aich, Ankit and
Quynh, Avery and
Pinkham, Amy and
Harvey, Philip and
Depp, Colin and
Parde, Natalie",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.1430",
pages = "16457--16472",
abstract = "Contemporary NLP has rapidly progressed from feature-based classification to fine-tuning and prompt-based techniques leveraging large language models. Many of these techniques remain understudied in the context of real-world, clinically enriched spontaneous dialogue. We fill this gap by systematically testing the efficacy and overall performance of a wide variety of NLP techniques ranging from feature-based to in-context learning on transcribed speech collected from patients with bipolar disorder, schizophrenia, and healthy controls taking a focused, clinically-validated language test. We observe impressive utility of a range of feature-based and language modeling techniques, finding that these approaches may provide a plethora of information capable of upholding clinical truths about these subjects. Building upon this, we establish pathways for future research directions in automated detection and understanding of psychiatric conditions.",
}
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%0 Conference Proceedings
%T Towards Comprehensive Language Analysis for Clinically Enriched Spontaneous Dialogue
%A Karacan, Baris
%A Aich, Ankit
%A Quynh, Avery
%A Pinkham, Amy
%A Harvey, Philip
%A Depp, Colin
%A Parde, Natalie
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F karacan-etal-2024-towards-comprehensive
%X Contemporary NLP has rapidly progressed from feature-based classification to fine-tuning and prompt-based techniques leveraging large language models. Many of these techniques remain understudied in the context of real-world, clinically enriched spontaneous dialogue. We fill this gap by systematically testing the efficacy and overall performance of a wide variety of NLP techniques ranging from feature-based to in-context learning on transcribed speech collected from patients with bipolar disorder, schizophrenia, and healthy controls taking a focused, clinically-validated language test. We observe impressive utility of a range of feature-based and language modeling techniques, finding that these approaches may provide a plethora of information capable of upholding clinical truths about these subjects. Building upon this, we establish pathways for future research directions in automated detection and understanding of psychiatric conditions.
%U https://aclanthology.org/2024.lrec-main.1430
%P 16457-16472
Markdown (Informal)
[Towards Comprehensive Language Analysis for Clinically Enriched Spontaneous Dialogue](https://aclanthology.org/2024.lrec-main.1430) (Karacan et al., LREC-COLING 2024)
ACL
- Baris Karacan, Ankit Aich, Avery Quynh, Amy Pinkham, Philip Harvey, Colin Depp, and Natalie Parde. 2024. Towards Comprehensive Language Analysis for Clinically Enriched Spontaneous Dialogue. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 16457–16472, Torino, Italia. ELRA and ICCL.