Pre-trained Transformer-based Classification and Span Detection Models for Social Media Health Applications

Yuting Guo, Y. Ge, M. Ali Al-Garadi, A. Sarker
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引用次数: 5

Abstract

This paper describes our approach for six classification tasks (Tasks 1a, 3a, 3b, 4 and 5) and one span detection task (Task 1b) from the Social Media Mining for Health (SMM4H) 2021 shared tasks. We developed two separate systems for classification and span detection, both based on pre-trained Transformer-based models. In addition, we applied oversampling and classifier ensembling in the classification tasks. The results of our submissions are over the median scores in all tasks except for Task 1a. Furthermore, our model achieved first place in Task 4 and obtained a 7% higher F1-score than the median in Task 1b.
社交媒体健康应用中基于预训练变压器的分类和跨度检测模型
本文描述了我们对来自社交媒体挖掘健康(SMM4H) 2021共享任务的六个分类任务(任务1a, 3a, 3b, 4和5)和一个跨度检测任务(任务1b)的方法。我们开发了两个独立的分类和跨度检测系统,都是基于预训练的基于transformer的模型。此外,我们在分类任务中应用了过采样和分类器集成。除了Task 1a,我们提交的成绩都在中位数以上。此外,我们的模型在任务4中获得了第一名,并获得了比任务1b中位数高7%的f1分数。
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