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• W4313201287 abstract "Detecting suicidal tendencies and preventing suicides is an important social goal. The rise and continuance of emotion, the emotion category, and the intensity of the emotion are important clues about suicidal tendencies. The three determinants of emotion, viz. Valence, Arousal, and Dominance (VAD) can help determine a person’s exact emotion(s) and its intensity. This paper introduces an end-to-end VAD-assisted transformer-based multi-task network for detecting emotion (primary task) and its intensity (auxiliary task) in suicide notes. As part of this research, we expand the utility of the emotion-annotated benchmark dataset of suicide notes, CEASE-v2.0, by annotating all its sentences with emotion intensity labels. Empirical results show that our multi-task method performs better than the corresponding single-task systems, with the best attained overall Mean Recall (MR) of 65.25% on the emotion task. On a similar task, we improved MR by 8.78% over the existing state-of-the-art system. We evaluated our approach on three benchmark datasets for three different tasks. We observed that the introduced method consistently outperformed existing state-of-the-art approaches on the studied datasets, demonstrating its capacity to generalize to other downstream correlated tasks. We qualitatively examined our model’s output by comparing it to the labeling of a psychiatrist." @default.
• W4313201287 created "2023-01-06" @default.
• W4313201287 creator A5051718913 @default.
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• W4313201287 date "2023-03-01" @default.
• W4313201287 modified "2023-09-25" @default.
• W4313201287 title "VAD-assisted multitask transformer framework for emotion recognition and intensity prediction on suicide notes" @default.
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• W4313201287 doi "https://doi.org/10.1016/j.ipm.2022.103234" @default.
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