Biomedical and Clinical English Model Packages in the Stanza Python NLP Library
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Abstract (arXiv)
We introduce biomedical and clinical English model packages for the Stanza Python NLP library. These packages offer accurate syntactic analysis and named entity recognition capabilities for biomedical and clinical text, by combining Stanza's fully neural architecture with a wide variety of open datasets as well as large-scale unsupervised biomedical and clinical text data. We show via extensive experiments that our packages achieve syntactic analysis and named entity recognition performance that is on par with or surpasses state-of-the-art results. We further show that these models do not compromise speed compared to existing toolkits when GPU acceleration is available, and are made easy to download and use with Stanza's Python interface. A demonstration of our packages is available at: http://stanza.run/bio.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Jul 2020.
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Citing papers per year: 2021: 2; 2022: 0; 2023: 1.
3 papers.
- 2021Spark NLP: Natural Language Understanding at Scalecited by 16
- 2023Rethinking Document-Level Relation Extraction: A Reality Checkcited by 4
- 2021Syntax Matters! Syntax-Controlled in Text Style Transfercited by 3
- 2023Rethinking Document-Level Relation Extraction: A Reality Check15 Jun 2023
- 2021Syntax Matters! Syntax-Controlled in Text Style Transfer12 Aug 2021
- 2021Spark NLP: Natural Language Understanding at Scale26 Jan 2021