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名词-名词组合:2160 对单词对的有意义评分和词汇统计。

Noun-noun combination: meaningfulness ratings and lexical statistics for 2,160 word pairs.

机构信息

Department of Psychology, Rutgers University, Smith Hall Room 337, 101 Warren Street, Newark, NJ 07102, USA.

出版信息

Behav Res Methods. 2013 Jun;45(2):463-9. doi: 10.3758/s13428-012-0256-3.

Abstract

The combining of individual concepts to form an emergent concept is a fundamental aspect of language, yet much less is known about it than about processing isolated words or sentences. To facilitate research on conceptual combination, we provide meaningfulness ratings for a large set of (2,160) noun-noun pairs. Half of these pairs (1,080) are reversed versions of the other half (e.g., SKI JACKET and JACKET SKI), to facilitate the comparison of successful and unsuccessful conceptual combination independently of constituent lexical items. The computer code used for obtaining these ratings through a Web interface is provided. To further enhance the usefulness of this resource, ancillary measures obtained from other sources are also provided for each pair. These measures include associate production norms, contextual relatedness in terms of latent semantic analysis distance, total number of letters, phrase-level usage frequency, and word-level usage frequency summed across the words in each pair. Results of correlation and regression analyses are also provided for a quantitative description of the stimulus set. A subset of these stimuli was used to identify neural correlates of successful conceptual combination Graves, Binder, Desai, Conant, & Seidenberg, (NeuroImage 53:638-646, 2010). The stimuli can be used in other research and also provide benchmark data for evaluating the effectiveness of computational algorithms for predicting meaningfulness of noun-noun pairs.

摘要

将单个概念组合成一个新的整体概念是语言的一个基本方面,但人们对它的了解远不如对孤立单词或句子的处理了解得多。为了促进对概念组合的研究,我们提供了大量(2160)名词-名词对的有意义性评分。这些对的一半(1080)是另一半的反转版本(例如,SKI JACKET 和 JACKET SKI),以便在不考虑组成词汇的情况下独立比较成功和不成功的概念组合。提供了通过 Web 界面获得这些评分的计算机代码。为了进一步增强此资源的有用性,还为每一对提供了其他来源获得的辅助措施。这些措施包括联想产生规范、潜在语义分析距离方面的上下文相关性、总字母数、短语级使用频率以及每个对中单词的单词级使用频率。还提供了相关和回归分析的结果,以定量描述刺激集。这些刺激的一个子集被用于识别成功概念组合的神经相关性 Graves、Binder、Desai、Conant 和 Seidenberg(NeuroImage 53:638-646, 2010)。这些刺激可用于其他研究,也可提供基准数据,用于评估用于预测名词-名词对有意义性的计算算法的有效性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2eae/3663253/c414ac58b5f2/13428_2012_256_Fig2_HTML.jpg

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