协助基于内容的文档标记和分类

K. Wrona, S. Oudkerk, A. Armando, Silvio Ranise, Riccardo Traverso, Lisa Ferrari, Richard McEvoy
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引用次数: 9

摘要

在现代军事系统中,对所有信息的来源进行正确标记是有效的信息访问控制的关键因素。如果信息没有正确标记,就不能在不同的利益团体和联盟伙伴之间共享,这会影响共享的责任,并可能阻碍正在进行的军事行动。本文描述了在北约通信和信息局进行的两项实验,这些实验与支持对已有和新创建的信息对象进行正确标记有关。使用了两种不同的技术,一种基于语义分析,另一种基于机器学习。这两种方法在各自的用例场景中都提供了有希望的结果,但是在操作部署之前需要进一步的开发。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Assisted content-based labelling and classification of documents
The correct labelling of all information at its point of origin is a critical enabler for effective information access control in modern military systems. If information is not properly labeled it cannot be shared between different communities of interest and coalition partners, which affects the responsibility to share and potentially impedes ongoing military operations. This paper describes two experiments performed at the NATO Communications and Information Agency related to supporting correct labelling of both pre-existing and newly created information objects. Two different techniques are used, one based on semantic analysis and the other on machine learning. Both approaches offer promising results in their respective use case scenarios, but require further development prior to operational deployment.
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