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一个新的词汇替换和单词混合错误语料库:探究词元提取失败的语义结构。

A New Corpus of Lexical Substitution and Word Blend Errors: Probing the Semantic Structure of Lemma Access Failures.

作者信息

Alderete John, Baese-Berk Melissa, Brasoveanu Adrian, Law Jess H K

机构信息

Linguistics, Cognitive Science, Simon Fraser University, Burnaby, V5A 1S6, Canada.

University of Oregon, Eugene, United States.

出版信息

J Cogn. 2023 May 18;6(1):26. doi: 10.5334/joc.278. eCollection 2023.

Abstract

Models of lemma access in language production predict occasional mis-selection of lemmas linked to highly similar concepts (synonyms) and concepts standing in a set-superset relation (subsumatives). It is unclear, however, if such errors occur in spontaneous speech, and if they do, whether humans can detect them given their minimal impact on sentence meaning. This data report examines a large corpus of English spontaneous speech errors and documents a low but non-negligible occurrence of these categories. The existence of synonym and subsumative errors is documented in a larger open access data set that supports a range of new investigations of the semantic structure of lexical substitution and word blend speech errors.

摘要

语言生成中词元提取模型预测,与高度相似概念(同义词)以及处于集合-超集关系(包含关系)的概念相联系的词元偶尔会被错误选择。然而,尚不清楚此类错误是否会出现在自然言语中,如果出现了,鉴于它们对句子意义的影响极小,人类是否能够察觉。本数据报告考察了大量英语自然言语错误语料库,并记录了这些类别错误的低发生率但并非可以忽略不计。在一个更大的开放获取数据集中记录了同义词和包含关系错误的存在,该数据集支持对词汇替换和单词混合言语错误的语义结构进行一系列新的研究。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3890/10198225/9f818f92e82f/joc-6-1-278-g1.jpg

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