Numerical Assessment of Orthographic Neighbourhood Size Fluctuation in Writing Using Fractal Dimension Analysis

IF 0.7 2区文学 0 LANGUAGE & LINGUISTICS

Journal of Quantitative Linguistics Pub Date : 2019-11-25 DOI:10.1080/09296174.2019.1694360

R. Taibu, E. Cheung, Weier Ye, S. Dehipawala, V. Shekoyan, G. Tremberger, T. Cheung

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引用次数: 1

Abstract

ABSTRACT The orthographic size of a targeted word, the number of new words that can be generated from a targeted word by exchanging a single letter, offers a research window where words can be transformed into numerical values. The CLEARPOND technology from Northwestern University was used for the transformation. A writing can then be modelled as a time series where the fluctuation can be further described using fractal dimension analysis. This project used the Higuchi fractal method for the computation of the fractal dimensions of time series. The proof of concept was conducted using writing examples which include Astronomy writing and English writing, the responses of Trump and Clinton in a Presidential election debate, and song lyrics. The results suggested that a high fractal dimension has an association with a high-demand cognitive task. The use of fractal dimension analysis as a writing assessment tool is discussed with relationship to the current lexical diversity computation technology.

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应用分形分析对书写中正交邻域大小波动的数值评估

摘要目标词的正字法大小，即通过交换单个字母可以从目标词中生成的新词数量，为将单词转换为数值提供了一个研究窗口。改造采用了西北大学CLEARPOND技术。然后可以将书写建模为时间序列，其中可以使用分形维数分析来进一步描述波动。该项目使用Higuchi分形方法计算时间序列的分形维数。概念验证使用了写作示例，包括天文学写作和英语写作、特朗普和克林顿在总统选举辩论中的回应以及歌词。结果表明，高分形维数与高需求的认知任务有关。结合当前的词汇多样性计算技术，讨论了分形维数分析作为写作评估工具的应用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Quantitative Linguistics Multiple-

CiteScore

2.90

自引率

7.10%

发文量

期刊介绍： The Journal of Quantitative Linguistics is an international forum for the publication and discussion of research on the quantitative characteristics of language and text in an exact mathematical form. This approach, which is of growing interest, opens up important and exciting theoretical perspectives, as well as solutions for a wide range of practical problems such as machine learning or statistical parsing, by introducing into linguistics the methods and models of advanced scientific disciplines such as the natural sciences, economics, and psychology.