BERN2：一种先进的神经生物医学命名实体识别和标准化工具。
BERN2: an advanced neural biomedical named entity recognition and normalization tool.
机构信息
Department of Computer Science and Engineering, Korea University, Seoul 02841, Republic of Korea.
AIRS Company, Hyundai Motor Group, Seoul 06620, Republic of Korea.
出版信息
Bioinformatics. 2022 Oct 14;38(20):4837-4839. doi: 10.1093/bioinformatics/btac598.
UNLABELLED
In biomedical natural language processing, named entity recognition (NER) and named entity normalization (NEN) are key tasks that enable the automatic extraction of biomedical entities (e.g. diseases and drugs) from the ever-growing biomedical literature. In this article, we present BERN2 (Advanced Biomedical Entity Recognition and Normalization), a tool that improves the previous neural network-based NER tool by employing a multi-task NER model and neural network-based NEN models to achieve much faster and more accurate inference. We hope that our tool can help annotate large-scale biomedical texts for various tasks such as biomedical knowledge graph construction.
AVAILABILITY AND IMPLEMENTATION
Web service of BERN2 is publicly available at http://bern2.korea.ac.kr. We also provide local installation of BERN2 at https://github.com/dmis-lab/BERN2.
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
未加标签
在生物医学自然语言处理中，命名实体识别（NER）和命名实体标准化（NEN）是关键任务，可实现从不断增长的生物医学文献中自动提取生物医学实体（例如疾病和药物）。在本文中，我们提出了 BERN2（高级生物医学实体识别和标准化），这是一种工具，通过使用多任务 NER 模型和基于神经网络的 NEN 模型改进了以前基于神经网络的 NER 工具，从而实现更快、更准确的推断。我们希望我们的工具可以帮助注释大规模的生物医学文本，以用于各种任务，例如生物医学知识图的构建。
可用性和实现
BERN2 的 Web 服务可在 http://bern2.korea.ac.kr 上公开获得。我们还在 https://github.com/dmis-lab/BERN2 上提供了 BERN2 的本地安装。
补充信息
补充数据可在生物信息学在线获得。
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