The BioCreative VII Track 3 challenge focused on the identification of medication names in Twitter user timelines. For our submission to this challenge, we expanded the available training data by using several data augmentation techniques. The augmented data was then used to fine-tune an ensemble of language models that had been pre-trained on general-domain Twitter content. The proposed approach outperformed the prior state-of-the-art algorithm Kusuri and ranked high in the competition for our selected objective function, overlapping F1 score.

翻译：生物医学第七轨第三轨挑战侧重于在Twitter用户时间表中识别药物名称。为了迎接这一挑战,我们通过使用若干数据增强技术扩大了现有培训数据。随后,扩充后的数据被用来微调在一般域Twitter内容方面经过预先培训的一整套语言模式。拟议方法比以往最先进的Kusuri算法表现得要好,在选择的F1评分中排名高。