ULD-NUIG at Social Media Mining for Health Applications (#SMM4H) Shared Task 2021

Atul Kr. Ojha, P. Rani, Koustava Goswami, Bharathi Raja Chakravarthi, John P. Mccrae
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引用次数: 0

Abstract

Social media platforms such as Twitter and Facebook have been utilised for various research studies, from the cohort-level discussion to community-driven approaches to address the challenges in utilizing social media data for health, clinical and biomedical information. Detection of medical jargon’s, named entity recognition, multi-word expression becomes the primary, fundamental steps in solving those challenges. In this paper, we enumerate the ULD-NUIG team’s system, designed as part of Social Media Mining for Health Applications (#SMM4H) Shared Task 2021. The team conducted a series of experiments to explore the challenges of task 6 and task 5. The submitted systems achieve F-1 0.84 and 0.53 score for task 6 and 5 respectively.
社交媒体挖掘健康应用(#SMM4H)共享任务2021
Twitter和Facebook等社交媒体平台已被用于各种研究,从群体层面的讨论到社区驱动的方法,以应对利用社交媒体数据获取健康、临床和生物医学信息方面的挑战。医学术语的检测、命名实体识别、多词表达成为解决这些挑战的首要、基本步骤。在本文中,我们列举了ld - nuig团队的系统,该系统被设计为健康应用社交媒体挖掘(#SMM4H)共享任务2021的一部分。该团队进行了一系列实验来探索任务6和任务5的挑战。提交的系统在任务6和任务5的得分分别为F-1 0.84和0.53。
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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