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        <title>ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing</title>
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        <namePart type="given">Mark</namePart>
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            <title>Proceedings of the 18th BioNLP Workshop and Shared Task</title>
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            <namePart type="given">Dina</namePart>
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            <namePart type="given">Junichi</namePart>
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    <abstract>Despite recent advances in natural language processing, many statistical models for processing text perform extremely poorly under domain shift. Processing biomedical and clinical text is a critically important application area of natural language processing, for which there are few robust, practical, publicly available models. This paper describes scispaCy, a new Python library and models for practical biomedical/scientific text processing, which heavily leverages the spaCy library. We detail the performance of two packages of models released in scispaCy and demonstrate their robustness on several tasks and datasets. Models and code are available at https://allenai.github.io/scispacy/.</abstract>
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%0 Conference Proceedings
%T ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing
%A Neumann, Mark
%A King, Daniel
%A Beltagy, Iz
%A Ammar, Waleed
%Y Demner-Fushman, Dina
%Y Cohen, Kevin Bretonnel
%Y Ananiadou, Sophia
%Y Tsujii, Junichi
%S Proceedings of the 18th BioNLP Workshop and Shared Task
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F neumann-etal-2019-scispacy
%X Despite recent advances in natural language processing, many statistical models for processing text perform extremely poorly under domain shift. Processing biomedical and clinical text is a critically important application area of natural language processing, for which there are few robust, practical, publicly available models. This paper describes scispaCy, a new Python library and models for practical biomedical/scientific text processing, which heavily leverages the spaCy library. We detail the performance of two packages of models released in scispaCy and demonstrate their robustness on several tasks and datasets. Models and code are available at https://allenai.github.io/scispacy/.
%R 10.18653/v1/W19-5034
%U https://aclanthology.org/W19-5034
%U https://doi.org/10.18653/v1/W19-5034
%P 319-327

Markdown (Informal)

[ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing](https://aclanthology.org/W19-5034) (Neumann et al., BioNLP 2019)

• ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing (Neumann et al., BioNLP 2019)

ACL

• Mark Neumann, Daniel King, Iz Beltagy, and Waleed Ammar. 2019. ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing. In Proceedings of the 18th BioNLP Workshop and Shared Task, pages 319–327, Florence, Italy. Association for Computational Linguistics.