Biomedical NER for the Enterprise with Distillated BERN2 and the Kazu Framework
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Abstract (arXiv)
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
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 10 of its 28 references that are in the map. First posted on arXiv 1 Dec 2022.
Builds on
10 of 28 references.
References · 10
- 2015Neural Machine Translation of Rare Words with Subword Unitscited by 2,918
- 2019BioBERT: a pre-trained biomedical language representation model for biomedical text miningcited by 1,947
- 2019TinyBERT: Distilling BERT for Natural Language Understandingcited by 1,167
- 2017Ray: A Distributed Framework for Emerging AI Applicationscited by 527
- 2019ScispaCy: Fast and Robust Models for Biomedical Natural Language Processingcited by 244
- 2020Self-Alignment Pretraining for Biomedical Entity Representationscited by 237
- 2022BERN2: an advanced neural biomedical named entity recognition and normalization toolcited by 42
- 2021Named Entity Recognition with Small Strongly Labeled and Large Weakly Labeled Datacited by 18
- 2018CollaboNet: collaboration of deep neural networks for biomedical named entity recognitioncited by 10
- 2021How Do Your Biomedical Named Entity Recognition Models Generalize to Novel Entities?cited by 7
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Citing papers per year: 2024: 2; 2025: 0; 2026: 1.
3 papers.