基于网络帖子的自杀风险评估的神经特征融合和预测类别概率

Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology Pub Date : 2019-06-01 DOI:10.18653/v1/W19-3004

Elham Mohammadi, Hessam Amini, Leila Kosseim

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引用次数: 21

摘要

本文总结了我们参与CLPsych 2019共享任务(CLaC)的情况。共享任务的目标是根据网上帖子的集合来检测和评估自杀风险。对于我们的参与，我们使用了一种集成方法，该方法利用8个神经子模型来提取神经特征并预测类别概率，然后由SVM分类器使用。我们团队在3个任务中有2个(任务A和C)获得了第一名。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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CLaC at CLPsych 2019: Fusion of Neural Features and Predicted Class Probabilities for Suicide Risk Assessment Based on Online Posts

This paper summarizes our participation to the CLPsych 2019 shared task, under the name CLaC. The goal of the shared task was to detect and assess suicide risk based on a collection of online posts. For our participation, we used an ensemble method which utilizes 8 neural sub-models to extract neural features and predict class probabilities, which are then used by an SVM classifier. Our team ranked first in 2 out of the 3 tasks (tasks A and C).

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Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology

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