SECNLP: A survey of embeddings in clinical natural language processing.,Journal of Biomedical informatics

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SECNLP: A survey of embeddings in clinical natural language processing.
Journal of Biomedical informatics ( IF 4.0 ) Pub Date : 2019-11-08 , DOI: 10.1016/j.jbi.2019.103323
Katikapalli Subramanyam Kalyan ₁ , S Sangeetha ₁

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Distributed vector representations or embeddings map variable length text to dense fixed length vectors as well as capture prior knowledge which can transferred to downstream tasks. Even though embeddings have become de facto standard for text representation in deep learning based NLP tasks in both general and clinical domains, there is no survey paper which presents a detailed review of embeddings in Clinical Natural Language Processing. In this survey paper, we discuss various medical corpora and their characteristics, medical codes and present a brief overview as well as comparison of popular embeddings models. We classify clinical embeddings and discuss each embedding type in detail. We discuss various evaluation methods followed by possible solutions to various challenges in clinical embeddings. Finally, we conclude with some of the future directions which will advance research in clinical embeddings.

中文翻译：

SECNLP：临床自然语言处理中的嵌入调查。

分布式矢量表示或嵌入将可变长度的文本映射到密集的固定长度矢量，并捕获可以转移到下游任务的先验知识。即使嵌入已成为通用和临床领域中基于深度学习的NLP任务中文本表示的事实上标准，也没有调查论文对临床自然语言处理中的嵌入进行了详细介绍。在本调查文件中，我们讨论了各种医疗语料库及其特征，医疗法规，并提供了简要概述以及流行的嵌入模型的比较。我们对临床嵌入进行分类，并详细讨论每种嵌入类型。我们讨论了各种评估方法，然后针对临床嵌入中的各种挑战提出了可能的解决方案。最后，

更新日期：2019-11-08

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