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计数原则使数字单词具有唯一性。

The counting principle makes number words unique.

作者信息

Ariel Mira, Levshina Natalia

机构信息

Department of Linguistics, Tel Aviv University, Tel Aviv, Israel.

Neurobiology of language, Max Planck Institute for Psycholinguistics, Nijmegen, Netherlands.

出版信息

Corpus Linguist Linguist Theory. 2024 Mar 29;21(1):173-199. doi: 10.1515/cllt-2023-0105. eCollection 2025 Feb.

DOI:10.1515/cllt-2023-0105
PMID:39925873
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11806440/
Abstract

Following Ariel (2021. Why it's hard to construct ad hoc number concepts. In Caterina Mauri, Ilaria Fiorentini, & Eugenio Goria (eds.), , 439-462. Amsterdam: John Benjamins), we argue that number words manifest distinct distributional patterns from open-class lexical items. When modified, open-class words typically take selectors (as in ), which select a subset of their potential denotations (e.g., "nonprototypical table"). They are typically modified by loosening operators (e.g., ), since even if bare, typical lexemes can broaden their interpretation (e.g., referring to a rock used as a table). Number words, on the other hand, have a single, precise meaning and denotation and cannot take a selector, which would need to select a subset of their (single) denotation (??). However, they are often overtly broadened (), creating a range of values around . First, we extend Ariel's empirical examination to the larger COCA and to Hebrew (HeTenTen). Second, we propose that open-class and number words belong to sparse versus dense lexical domains, respectively, because the former exhibit prototypicality effects, but the latter do not. Third, we further support the contrast between sparse and dense domains by reference to: synchronic models of sparse and dense lexemes, which testify to their differential distributions, numeral use in noncounting communities, and different renewal rates for the two lexical types.

摘要

参照阿里尔(2021年《构建临时数字概念为何困难》,载于卡特琳娜·毛里、伊拉里亚·菲奥伦蒂尼和欧金尼奥·戈里亚编著的《[书名缺失]》,第439 - 462页。阿姆斯特丹:约翰·本杰明斯出版社)的观点,我们认为数字词呈现出与开放类词汇项不同的分布模式。当开放类词被修饰时,通常会采用选择词(如在“[具体例子缺失]”中),这些选择词会挑选出其潜在语义的一个子集(例如,“非典型桌子”)。它们通常由宽松算子修饰(例如,“[具体例子缺失]”),因为即使是光秃秃的典型词元也可以拓宽其解释范围(例如,“[具体例子缺失]”指用作桌子的一块石头)。另一方面,数字词具有单一、精确的意义和语义,不能采用选择词,因为选择词需要挑选出其(单一)语义的一个子集(??)。然而,它们经常会被明显拓宽(“[具体例子缺失]”),围绕“[具体例子缺失]”创造出一系列值。首先,我们将阿里尔的实证研究扩展到更大规模的美国当代英语语料库(COCA)以及希伯来语(HeTenTen)。其次,我们提出开放类词和数字词分别属于稀疏词汇域和密集词汇域,因为前者呈现出原型效应,而后者则没有。第三,我们通过参考以下内容进一步支持稀疏域和密集域之间的对比:稀疏和密集词元的共时模型,这些模型证明了它们的不同分布;非计数群体中的数字使用情况;以及这两种词汇类型不同的更新率。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b52/11806440/a0f1a7efa76e/j_cllt-2023-0105_fig_005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b52/11806440/289839651a8c/j_cllt-2023-0105_fig_001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b52/11806440/7e811aec6bae/j_cllt-2023-0105_fig_002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b52/11806440/f257a20d4cd1/j_cllt-2023-0105_fig_003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b52/11806440/1ec7b5009681/j_cllt-2023-0105_fig_004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b52/11806440/a0f1a7efa76e/j_cllt-2023-0105_fig_005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b52/11806440/289839651a8c/j_cllt-2023-0105_fig_001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b52/11806440/7e811aec6bae/j_cllt-2023-0105_fig_002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b52/11806440/f257a20d4cd1/j_cllt-2023-0105_fig_003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b52/11806440/1ec7b5009681/j_cllt-2023-0105_fig_004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b52/11806440/a0f1a7efa76e/j_cllt-2023-0105_fig_005.jpg

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