论文标题
带有蒸馏的Bern2和Kazu框架的企业的生物医学NER
Biomedical NER for the Enterprise with Distillated BERN2 and the Kazu Framework
论文作者
论文摘要
为了协助药物发现/开发过程，制药公司经常应用生物医学NER和链接技术，而不是内部和公共语料库。对Bionlp领域的数十年研究产生了大量算法，系统和数据集。但是，我们的经验是，没有一个开源系统满足现代制药公司的所有要求。在这项工作中，我们根据我们对行业的经验来描述这些要求，并展示了Kazu，这是一个高度可扩展的，可扩展的开源框架，旨在为制药领域提供Bionlp。 Kazu是围绕BERN2 NER模型（TinyBern2）的计算高效版本建造的，随后将其他几种Bionlp技术包装到一个连贯的系统中。 Kazu框架是开源的：https：//github.com/astrazeneca/kazu
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
