  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

Computation and Language