Augmenting Biomedical Named Entity Recognition with General-domain Resources.

ArXiv Pub Date : 2024-11-03
Yu Yin, Hyunjae Kim, Xiao Xiao, Chih Hsuan Wei, Jaewoo Kang, Zhiyong Lu, Hua Xu, Meng Fang, Qingyu Chen
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Abstract

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.

利用通用领域资源增强生物医学命名实体识别。
训练基于神经网络的生物医学命名实体识别(BioNER)模型通常需要大量昂贵的人工标注。虽然有几项研究利用多个 BioNER 数据集进行多任务学习以减少人力,但这种方法并不能持续提高性能,而且可能会在不同的生物医学语料库中引入标签模糊性。我们的目标是通过从与生物医学数据集概念重叠较少且易于获取的资源中进行迁移学习来应对这些挑战。在本文中,我们提出了 GERBERA,一种利用通用领域 NER 数据集进行训练的简单而有效的方法。具体来说,我们采用多任务学习方法,利用目标 BioNER 数据集和通用域数据集训练一个预先训练好的生物医学语言模型。随后,我们专门针对 BioNER 数据集对模型进行了微调。我们在八个实体类型的五个数据集上对 GERBERA 进行了系统评估,这些数据集共包含 81,410 个实例。尽管使用的生物医学资源较少,但与使用其他多个 BioNER 数据集训练的基线模型相比,我们的模型表现出了卓越的性能。具体来说,我们的模型在八种实体类型中的六种类型上始终优于基线模型,与来自五个不同语料库的八种生物医学实体类型的最佳基线性能相比,平均提高了 0.9%。我们的方法在数据有限的 BioNER 数据集上尤其有效,在 JNLPBA-RNA 数据集上的 F1 分数提高了 4.7%。
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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