分类:ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing

Mark Neumann, Daniel King, Iz Beltagy, Waleed Ammar. ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing. BioNLP@ACL 2019

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/.

总结和评论

这篇文章发展了一套用于科研论文的概念抽取和概念关系挖掘的scispaCy软件，其本身基于spaCy软件。

这套软件及其背后的方法，用于概念地图半自动构建也是可以的。

概念地图