医学文献中的“意义单位”:自然语言处理视角

D. Popolov, Joseph R. Barr
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引用次数: 1

摘要

本文讨论了自然语言处理(NLP)系统的设计原则,用于从医生笔记、实验室结果和其他自由格式的医疗文档中自动提取数据。我们认为,与其在文本中搜索“意义的原子单位”,然后试图通过日益复杂的规则系统将它们概括为更广泛的文档集,NLP从业者应该将概念作为一个整体作为文本的有意义单位。这简化了规则,使NLP系统更容易维护和适应。出发点纯粹是实用的,然而,对此类系统实施的典型问题进行更深入的调查将使我们讨论NLP实践背后的更广泛的理论原则。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
"Units of Meaning" in Medical Documents: Natural Language Processing Perspective
This paper discusses principles for the design of natural language processing (NLP) systems to automatically extract of data from doctor's notes, laboratory results and other medical documents in free-form text. We argue that rather than searching for 'atom units of meaning' in the text and then trying to generalize them into a broader set of documents through increasingly complicated system of rules, an NLP practitioner should take concepts as a whole as a meaningful unit of text. This simplifies the rules and makes NLP system easier to maintain and adapt. The departure point is purely practical, however a deeper investigation of typical problems with the implementation of such systems leads us to a discussion of broader theoretical principles underlying the NLP practices.
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