Adverse Drug Effect and Personalized Health Mentions, CLaC at SMM4H 2019, Tasks 1 and 4

Parsa Bagherzadeh, Nadia Sheikh, S. Bergler
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引用次数: 2

Abstract

CLaC labs participated in Task 1 and 4 of SMM4H 2019. We pursed two main objectives in our submission. First we tried to use some textual features in a deep net framework, and second, the potential use of more than one word embedding was tested. The results seem positively affected by the proposed architectures.
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CLaC实验室参与了SMM4H 2019的任务1和4。我们提交意见书的目的主要有两个。首先,我们尝试在深度网络框架中使用一些文本特征,其次,测试了多个单词嵌入的潜在用途。结果似乎受到所提议的体系结构的积极影响。
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Approaching SMM4H with Merged Models and Multi-task Learning BIGODM System in the Social Media Mining for Health Applications Shared Task 2019 HITSZ-ICRC: A Report for SMM4H Shared Task 2019-Automatic Classification and Extraction of Adverse Effect Mentions in Tweets Lexical Normalization of User-Generated Medical Text Towards Text Processing Pipelines to Identify Adverse Drug Events-related Tweets: University of Michigan @ SMM4H 2019 Task 1
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