ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing

@article{Neumann2019ScispaCyFA,
  title={ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing},
  author={Mark Neumann and Daniel King and Iz Beltagy and Waleed Ammar},
  journal={ArXiv},
  year={2019},
  volume={abs/1902.07669}
}
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… Expand
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