Training a neural network-based biomedical named entity recognition (BioNER)
model usually requires extensive and costly human annotations. While several
studies have employed multi-task learning with multiple BioNER datasets to
reduce human effort, this approach does not consistently yield performance
improvements and may introduce label ambiguity in different biomedical corpora.
We aim to tackle those challenges through transfer learning from easily
accessible resources with fewer concept overlaps with biomedical datasets. In
this paper, we proposed GERBERA, a simple-yet-effective method that utilized a
general-domain NER dataset for training. Specifically, we performed multi-task
learning to train a pre-trained biomedical language model with both the target
BioNER dataset and the general-domain dataset. Subsequently, we fine-tuned the
models specifically for the BioNER dataset. We systematically evaluated GERBERA
on five datasets of eight entity types, collectively consisting of 81,410
instances. Despite using fewer biomedical resources, our models demonstrated
superior performance compared to baseline models trained with multiple
additional BioNER datasets. Specifically, our models consistently outperformed
the baselines in six out of eight entity types, achieving an average
improvement of 0.9% over the best baseline performance across eight biomedical
entity types sourced from five different corpora. Our method was especially
effective in amplifying performance on BioNER datasets characterized by limited
data, with a 4.7% improvement in F1 scores on the JNLPBA-RNA dataset.

通过利用通用领域的命名实体识别数据集进行迁移学习，本研究提出了一个简单而有效的方法 GERBERA，用于训练预训练的生物医学语言模型，该模型能在少量生物医学资源的情况下，对多个生物医学数据集进行命名实体识别，其表现优于使用多个附加生物医学数据集训练的基准模型。