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摘要翻译：
我们研究了信息语义的两个方面：（一）所有关系的收集，（二）跟踪和发现异常和变化。第一种是通过在一个公共投影空间中赋予所有相关信息空间一个欧几里德度量来实现的。第二个是由一个诱导的超流量计来模拟的。通过对应分析提供了一种实现基于交叉表计数（以及来自其他输入数据格式）的不同信息空间欧几里得嵌入的非常通用的方法。从那里，我们特别感兴趣的诱导超参数考虑到了数据的顺序--例如时间--排序。我们用这样一个视角来看待叙事，“思想的流动和语言的流动”（查菲）。在政策决策的应用中，我们展示了如何将分析集中在少量的维度上。
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英文标题：
《The Correspondence Analysis Platform for Uncovering Deep Structure in
Data and Information》
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作者：
Fionn Murtagh
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最新提交年份：
2008
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分类信息：
一级分类：Computer Science 计算机科学
二级分类：Artificial Intelligence 人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
We study two aspects of information semantics: (i) the collection of all relationships, (ii) tracking and spotting anomaly and change. The first is implemented by endowing all relevant information spaces with a Euclidean metric in a common projected space. The second is modelled by an induced ultrametric. A very general way to achieve a Euclidean embedding of different information spaces based on cross-tabulation counts (and from other input data formats) is provided by Correspondence Analysis. From there, the induced ultrametric that we are particularly interested in takes a sequential - e.g. temporal - ordering of the data into account. We employ such a perspective to look at narrative, "the flow of thought and the flow of language" (Chafe). In application to policy decision making, we show how we can focus analysis in a small number of dimensions.
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PDF链接：
https://arxiv.org/pdf/0807.0908
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