基于异构词嵌入组合的推文药物不良反应检测

Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task Pub Date : 2019-08-01 DOI:10.18653/v1/W19-3224

S. Aroyehun, Alexander Gelbukh

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引用次数: 7

摘要

本文详细介绍了我们在推特中检测药物不良反应报道的任务方法，这是2019年医疗保健应用共享任务的社交媒体挖掘的一部分。我们使用了三种类型的单词表示的组合作为LSTM模型的输入。通过这种方法，我们获得了0.5209的F1分数。

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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Detection of Adverse Drug Reaction in Tweets Using a Combination of Heterogeneous Word Embeddings

This paper details our approach to the task of detecting reportage of adverse drug reaction in tweets as part of the 2019 social media mining for healthcare applications shared task. We employed a combination of three types of word representations as input to a LSTM model. With this approach, we achieved an F1 score of 0.5209.

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Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task

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