arXiv Open Access 2022

Biomedical NER for the Enterprise with Distillated BERN2 and the Kazu Framework

Wonjin Yoon Richard Jackson Elliot Ford Vladimir Poroshin Jaewoo Kang
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Abstrak

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

Topik & Kata Kunci

Penulis (5)

W

Wonjin Yoon

R

Richard Jackson

E

Elliot Ford

V

Vladimir Poroshin

J

Jaewoo Kang

Format Sitasi

Yoon, W., Jackson, R., Ford, E., Poroshin, V., Kang, J. (2022). Biomedical NER for the Enterprise with Distillated BERN2 and the Kazu Framework. https://arxiv.org/abs/2212.00223

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Tahun Terbit
2022
Bahasa
en
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arXiv
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Open Access ✓