健康相关Twitter数据的迁移学习

Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task Pub Date : 1900-01-01 DOI:10.18653/v1/W19-3212

A. Dirkson, S. Verberne

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引用次数: 14

摘要

迁移学习在许多NLP应用中很有前景，特别是在标签数据有限的任务中。本文描述了TMRLeiden团队为2019年健康应用社交媒体挖掘(SMM4H)共享任务开发的方法。我们的方法使用最先进的迁移学习方法对药物不良反应(adr)进行分类、提取和规范化，并对与健康相关的推文中提到的个人健康进行分类。代码和经过微调的模型都是公开的。

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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Transfer Learning for Health-related Twitter Data

Transfer learning is promising for many NLP applications, especially in tasks with limited labeled data. This paper describes the methods developed by team TMRLeiden for the 2019 Social Media Mining for Health Applications (SMM4H) Shared Task. Our methods use state-of-the-art transfer learning methods to classify, extract and normalise adverse drug effects (ADRs) and to classify personal health mentions from health-related tweets. The code and fine-tuned models are publicly available.

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来源期刊

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

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