Biomedical NER for the Enterprise with Distillated BERN2 and the Kazu Framework
Wonjin Yoon, Richard Jackson, Elliot Ford, Vladimir Poroshin, Jaewoo Kang
Abstract
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.
BibTeX
@inproceedings{yoon-etal-2022-biomedical,
title = "Biomedical {NER} for the Enterprise with Distillated {BERN}2 and the Kazu Framework",
author = "Yoon, Wonjin and
Jackson, Richard and
Ford, Elliot and
Poroshin, Vladimir and
Kang, Jaewoo",
editor = "Li, Yunyao and
Lazaridou, Angeliki",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = dec,
year = "2022",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-industry.63/",
doi = "10.18653/v1/2022.emnlp-industry.63",
pages = "619--626"
}