Word Use Equivalence and Hierarchical Word Tiers

IF 0.7 2区 文学 0 LANGUAGE & LINGUISTICS Journal of Quantitative Linguistics Pub Date : 2022-10-13 DOI:10.1080/09296174.2022.2129377
Brent D. Burch, Jesse Egbert
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引用次数: 0

Abstract

ABSTRACT A ranked word list provides information about the position of each word in the list. However, retaining and employing the measure used to generate the ranked list can yield additional information about the words. If denotes the prevalence of a word in a corpus, then not only can the values of be ordered, their values can be compared to one another, and words having similar values can be grouped together into equivalence classes. Measures of word prevalence include mean text frequency, the dispersion of words across texts in a corpus, or a measure that combines frequency and dispersion. In this paper, we examine the concepts of word equivalence classes and hierarchical word tiers and apply these concepts to the words in the British National Corpus (BNC). Hierarchical word tiers can be constructed without the knowledge of all pairwise comparisons of the words under study. By grouping words that have similar values of prevalence, the ranked ordered list reduces to an informative set of hierarchical word tiers where each tier contains words that are similar to one another in terms of their use in the corpus.
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词汇使用等价和分层词层
摘要排名单词列表提供了每个单词在列表中的位置信息。然而,保留和使用用于生成排序列表的度量可以产生关于单词的附加信息。如果表示一个单词在语料库中的普遍性,那么不仅可以对的值进行排序,还可以将它们的值相互比较,并且可以将具有相似值的单词分组到等价类中。单词流行率的衡量标准包括平均文本频率、语料库中单词在文本中的分散度,或结合频率和分散度的衡量标准。在本文中,我们考察了单词等价类和分层单词层的概念,并将这些概念应用于英国国家语料库中的单词。在不了解所研究单词的所有成对比较的情况下,可以构建分层的单词层次。通过对具有相似流行率值的单词进行分组,经排序的列表简化为一组信息丰富的分层单词层,其中每一层包含在语料库中使用方面彼此相似的单词。
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
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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