企业的生物医学NER与蒸馏BERN2和Kazu框架

Wonjin Yoon, Richard Jackson, Elliot Ford, V. Poroshin, Jaewoo Kang
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引用次数: 1

摘要

为了协助药物发现/开发过程,制药公司经常在内部和公共语料库上应用生物医学NER和连接技术。数十年来对生物信息处理领域的研究产生了大量的算法、系统和数据集。然而,我们的经验是,没有一个单一的开源系统能够满足现代制药公司的所有需求。在这项工作中,我们根据我们的行业经验描述了这些需求,并提出了Kazu,一个高度可扩展、可扩展的开源框架,旨在支持制药行业的BioNLP。Kazu是围绕BERN2 NER模型(TinyBERN2)的计算效率版本构建的,随后将其他几种BioNLP技术整合到一个连贯的系统中。KAZU框架是开源的:https://github.com/AstraZeneca/KAZU
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Biomedical NER for the Enterprise with Distillated BERN2 and the Kazu Framework
In order to assist the drug discovery/development process, pharmaceutical companies often apply biomedical NER and linking techniques over internal and public corpora. Decades of study of the field of BioNLP has produced a plethora of algorithms, systems and datasets. However, our experience has been that no single open source system meets all the requirements of a modern pharmaceutical company. In this work, we describe these requirements according to our experience of the industry, and present Kazu, a highly extensible, scalable open source framework designed to support BioNLP for the pharmaceutical sector. Kazu is a built around a computationally efficient version of the BERN2 NER model (TinyBERN2), and subsequently wraps several other BioNLP technologies into one coherent system. KAZU framework is open-sourced: https://github.com/AstraZeneca/KAZU
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