A study on suicidal ideation detection based on domain knowledge and multi-head knowledge attention mechanism

Yidan Wang; Guiyun Zhang; Shaowei Zhang

doi:10.1117/12.3011348

8 November 2023 A study on suicidal ideation detection based on domain knowledge and multi-head knowledge attention mechanism

Yidan Wang, Guiyun Zhang, Shaowei Zhang

Author Affiliations +

Proceedings Volume 12923, Third International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2023); 129231S (2023) https://doi.org/10.1117/12.3011348
Event: 3rd International Conference on Artificial Intelligence, Virtual Reality and Visualization (AIVRV 2023), 2023, Chongqing, China

Abstract

To address the current problems of combining single domain-specific knowledge and poor fusion in suicidal ideation detection tasks, this paper proposes a Multi-Head Knowledge Attention Mechanism model that fuses domain knowledge (DK-MHKA) to fully integrate the suicide risk severity lexicon and the user's neurotic personality traits. The model involves integrating suicidal tendencies attributes into the semantic domain that encompasses the user's social media content, with the aim of enhancing the model's linguistic representations. Furthermore, the method employs a multi-head knowledge attention mechanism to effectively combine various sources of features, resulting in an enhanced predictive capability of the model. The experimental findings indicate that the suggested DK-MHKA model outperforms alternative baseline models in terms of forecasting precision. Additionally, the ablation experiments confirm the individual contributions of each module to the overall performance of the model.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yidan Wang, Guiyun Zhang, and Shaowei Zhang "A study on suicidal ideation detection based on domain knowledge and multi-head knowledge attention mechanism", Proc. SPIE 12923, Third International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2023), 129231S (8 November 2023); https://doi.org/10.1117/12.3011348

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KEYWORDS

Feature extraction

Performance modeling

Semantics

Data modeling

Web 2.0 technologies

Ablation

Head

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