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Quantity and Unit Extraction for Scientific and Technical Intelligence Analysis

机译:科技情报分析的数量和单位提取

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Scientific and Technical (S&T) intelligence analysts consume huge amounts of data to understand how scientific progress and engineering efforts affect current and future military capabilities. One of the most important types of information S&T analysts exploit is the quantities discussed in their source material. Frequencies, ranges, size, weight, power, and numerous other properties and measurements describing the performance characteristics of systems and the engineering constraints that define them must be culled from source documents before quantified analysis can begin. Automating the process of finding and extracting the relevant quantities from a wide range of S&T documents is difficult because information about quantities and their units is often contained in unstructured text with ad hoc conventions used to convey their meaning. Currently, even simple tasks, such as searching for documents discussing RF frequencies in a band of interest, is a labor intensive and error prone process. This research addresses the challenges facing development of a document processing capability that extracts quantities and units from S&T data, and how Natural Language Processing algorithms can be used to overcome these challenges.
机译:科学和技术(S&T)情报分析师使用大量数据来了解科学进步和工程成果如何影响当前和未来的军事能力。科技分析师利用的最重要的信息类型之一是其原始资料中讨论的数量。在开始量化分析之前,必须从源文档中剔除频率,范围,大小,重量,功率以及许多其他描述系统性能特征的性能和度量以及定义它们的工程约束。从大量的S&T文档中自动查找和提取相关数量的过程非常困难,因为有关数量及其单位的信息通常包含在非结构化文本中,并且使用临时约定来传达其含义。当前,即使是简单的任务,例如搜索讨论感兴趣频带中的RF频率的文档,也是一项劳动密集型且容易出错的过程。这项研究解决了从S&T数据中提取数量和单位的文档处理能力的发展所面临的挑战,以及如何使用自然语言处理算法来克服这些挑战。

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