论文标题
使用人工叙事理解（II）测试定量时空假设：建立不变概念，主题和名称空间的几何形状
Testing the Quantitative Spacetime Hypothesis using Artificial Narrative Comprehension (II) : Establishing the Geometry of Invariant Concepts, Themes, and Namespaces
论文作者
论文摘要
给定从传感器流选择的观测值库，可以通过多尺度过程来鲁棒性地表示，就不变概念和主题而言。将其应用于情节自然语言数据，可以获得与分解相关的图几何形状，这是事件的时空关系的直接编码。这项研究为语义时空假设的持续应用做出了贡献，并使用廉价的计算方法对叙事文本进行了无监督的分析。通过生物信息学分析的方式将数据流通过多尺度干涉法解析并分离为小成分。然后可以重新组合片段以构建原始的感觉发作 - 或仅基于四个基本的时空关系，通过结合和模式重建的化学反应形成新的叙述。生物信息学过程与自然语言的认知表示之间存在直接的对应关系。可以识别为“概念”和“叙事主题”的特征涵盖三个主尺度（Micro，Meso和Macro）。输入的片段充当字母层次结构的符号，该字母结构在每个尺度上定义了新的有效语言。
Given a pool of observations selected from a sensor stream, input data can be robustly represented, via a multiscale process, in terms of invariant concepts, and themes. Applying this to episodic natural language data, one may obtain a graph geometry associated with the decomposition, which is a direct encoding of spacetime relationships for the events. This study contributes to an ongoing application of the Semantic Spacetime Hypothesis, and demonstrates the unsupervised analysis of narrative texts using inexpensive computational methods without knowledge of linguistics. Data streams are parsed and fractionated into small constituents, by multiscale interferometry, in the manner of bioinformatic analysis. Fragments may then be recombined to construct original sensory episodes---or form new narratives by a chemistry of association and pattern reconstruction, based only on the four fundamental spacetime relationships. There is a straightforward correspondence between bioinformatic processes and this cognitive representation of natural language. Features identifiable as `concepts' and `narrative themes' span three main scales (micro, meso, and macro). Fragments of the input act as symbols in a hierarchy of alphabets that define new effective languages at each scale.
