词汇丰富度与语篇长度:基于熵的视角

IF 0.7 2区 文学 0 LANGUAGE & LINGUISTICS
Yaqian Shi, L. Lei
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引用次数: 15

摘要

摘要语篇长度是衡量词汇丰富度的一个主要问题,语篇长度对词汇丰富度的影响仍然存在争议。本研究旨在从熵的角度探讨语篇长度与词汇丰富度之间的关系。结果表明,随着文本长度的增加,词汇丰富度呈非线性增长模式。具体地说,词汇的丰富性随着文本的缩短而迅速增加。尽管文本长度不断扩大,但它很快就到达了一个稳定的边界点。Shannon估计的词汇丰富度的边界点约为1000个标记,Zhang估计的边界点较低且变化较大,包括500、800和1000个标记。这种稳定性可以用文本中单词概率的稳定性来解释。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Lexical Richness and Text Length: An Entropy-based Perspective
ABSTRACT Text length is a major concern in the measurement of lexical richness, and how lexical richness is affected by text length still remains open. The present study aims to explore the relation between text length and lexical richness from an entropy-based perspective. Results show a non-linear growth pattern of lexical richness by increasing text length. To be specific, lexical richness increases rapidly with shorter texts. It soon reaches a boundary point from which it stabilizes despite the continuous expansion of text length. The boundary point of the lexical richness by the Shannon estimation is around 1000 tokens and that by the Zhang estimation is lower and more varied, including 500, 800, and 1000 tokens. Such stability may be explained by the stabilization of word probability in the text.
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来源期刊
CiteScore
2.90
自引率
7.10%
发文量
7
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.
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