网络论坛自杀风险评估的语境表征

Ashwin Karthik Ambalavanan, P. D. Jagtap, Soumya Adhya, M. Devarakonda
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引用次数: 9

摘要

社交媒体上的帖子可能会提供有关主题(通常是作者)自杀风险和意图的线索,这些线索可以用于及时干预。在CLPsych 2019共享任务的推动下,这项研究开发了基于神经网络的方法,用于分析一个或多个Reddit论坛上的帖子,以评估主题的自杀风险。此任务带来的技术挑战之一是来自单个用户的多个帖子的大量文本。我们的神经网络模型使用先进的多头基于注意力的自编码器架构,称为变形金刚的双向编码器表示(BERT)。我们的系统在挑战的任务A上取得了0.477宏观平均F测量的第二好成绩。在我们为挑战开发的三个不同的替代方案中,处理所有用户帖子的单一BERT模型在所有三个任务上都表现最好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using Contextual Representations for Suicide Risk Assessment from Internet Forums
Social media posts may yield clues to the subject’s (usually, the writer’s) suicide risk and intent, which can be used for timely intervention. This research, motivated by the CLPsych 2019 shared task, developed neural network-based methods for analyzing posts in one or more Reddit forums to assess the subject’s suicide risk. One of the technical challenges this task poses is the large amount of text from multiple posts of a single user. Our neural network models use the advanced multi-headed Attention-based autoencoder architecture, called Bidirectional Encoder Representations from Transformers (BERT). Our system achieved the 2nd best performance of 0.477 macro averaged F measure on Task A of the challenge. Among the three different alternatives we developed for the challenge, the single BERT model that processed all of a user’s posts performed the best on all three Tasks.
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