基于ELMo的推文药物不良反应提及检测

S. Sarabadani
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引用次数: 6

摘要

本文描述了我们团队在SMM4H 2019共享任务中使用的模型。我们提交了子任务1和子任务2的结果。对于任务1,目的是检测含有药物不良反应(ADR)提及的推文,我们使用了ELMo嵌入,这是一种能够捕获句法和语义特征的深度上下文化单词表示。对于任务2,重点是提取ADR提及,首先使用与任务1相同的架构来识别tweet是否包含ADR。然后,对于正向分类为提及ADR的tweets,通过与3个不同的词典集的相似度匹配来识别相关的文本跨度。
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Detection of Adverse Drug Reaction Mentions in Tweets Using ELMo
This paper describes the models used by our team in SMM4H 2019 shared task. We submitted results for subtasks 1 and 2. For task 1 which aims to detect tweets with Adverse Drug Reaction (ADR) mentions we used ELMo embeddings which is a deep contextualized word representation able to capture both syntactic and semantic characteristics. For task 2, which focuses on extraction of ADR mentions, first the same architecture as task 1 was used to identify whether or not a tweet contains ADR. Then, for tweets positively classified as mentioning ADR, the relevant text span was identified by similarity matching with 3 different lexicon sets.
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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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