@inproceedings{boinepelli-etal-2022-towards,
title = "Towards Capturing Changes in Mood and Identifying Suicidality Risk",
author = "Boinepelli, Sravani and
Subramanian, Shivansh and
Singam, Abhijeeth and
Raha, Tathagata and
Varma, Vasudeva",
editor = "Zirikly, Ayah and
Atzil-Slonim, Dana and
Liakata, Maria and
Bedrick, Steven and
Desmet, Bart and
Ireland, Molly and
Lee, Andrew and
MacAvaney, Sean and
Purver, Matthew and
Resnik, Rebecca and
Yates, Andrew",
booktitle = "Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology",
month = jul,
year = "2022",
address = "Seattle, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.clpsych-1.24/",
doi = "10.18653/v1/2022.clpsych-1.24",
pages = "245--250",
abstract = "This paper describes our systems for CLPsych?s 2022 Shared Task. Subtask A involves capturing moments of change in an individual?s mood over time, while Subtask B asked us to identify the suicidality risk of a user. We explore multiple machine learning and deep learning methods for the same, taking real-life applicability into account while considering the design of the architecture. Our team achieved top results in different categories for both subtasks. Task A was evaluated on a post-level (using macro averaged F1) and on a window-based timeline level (using macro-averaged precision and recall). We scored a post-level F1 of 0.520 and ranked second with a timeline-level recall of 0.646. Task B was a user-level task where we also came in second with a micro F1 of 0.520 and scored third place on the leaderboard with a macro F1 of 0.380."
}
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%0 Conference Proceedings
%T Towards Capturing Changes in Mood and Identifying Suicidality Risk
%A Boinepelli, Sravani
%A Subramanian, Shivansh
%A Singam, Abhijeeth
%A Raha, Tathagata
%A Varma, Vasudeva
%Y Zirikly, Ayah
%Y Atzil-Slonim, Dana
%Y Liakata, Maria
%Y Bedrick, Steven
%Y Desmet, Bart
%Y Ireland, Molly
%Y Lee, Andrew
%Y MacAvaney, Sean
%Y Purver, Matthew
%Y Resnik, Rebecca
%Y Yates, Andrew
%S Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, USA
%F boinepelli-etal-2022-towards
%X This paper describes our systems for CLPsych?s 2022 Shared Task. Subtask A involves capturing moments of change in an individual?s mood over time, while Subtask B asked us to identify the suicidality risk of a user. We explore multiple machine learning and deep learning methods for the same, taking real-life applicability into account while considering the design of the architecture. Our team achieved top results in different categories for both subtasks. Task A was evaluated on a post-level (using macro averaged F1) and on a window-based timeline level (using macro-averaged precision and recall). We scored a post-level F1 of 0.520 and ranked second with a timeline-level recall of 0.646. Task B was a user-level task where we also came in second with a micro F1 of 0.520 and scored third place on the leaderboard with a macro F1 of 0.380.
%R 10.18653/v1/2022.clpsych-1.24
%U https://aclanthology.org/2022.clpsych-1.24/
%U https://doi.org/10.18653/v1/2022.clpsych-1.24
%P 245-250
Markdown (Informal)
[Towards Capturing Changes in Mood and Identifying Suicidality Risk](https://aclanthology.org/2022.clpsych-1.24/) (Boinepelli et al., CLPsych 2022)
ACL
- Sravani Boinepelli, Shivansh Subramanian, Abhijeeth Singam, Tathagata Raha, and Vasudeva Varma. 2022. Towards Capturing Changes in Mood and Identifying Suicidality Risk. In Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology, pages 245–250, Seattle, USA. Association for Computational Linguistics.