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Noun–noun combination: Meaningfulness ratings and lexical statistics for 2160 word pairs

机译:名词-名词组合:2160个单词对的意义等级和词汇统计

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摘要

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, ). 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.Electronic supplementary materialThe online version of this article (doi:10.3758/s13428-012-0256-3) contains supplementary material, which is available to authorized users.
机译:各个概念的组合形成一个紧急概念是语言的基本方面,但对语言的了解要比处理孤立的单词或句子少得多。为了促进概念组合的研究,我们为大量的(2,160)个名词-名词对提供了有意义的等级。这些对中的一半(1,080)是另一半的反向版本(例如,滑雪夹克和夹克滑雪),以便于独立于构成词项的比较成功和不成功的概念组合。提供了用于通过Web界面获得这些等级的计算机代码。为了进一步提高该资源的实用性,还为每对提供从其他来源获得的辅助措施。这些措施包括关联生产规范,根据潜在语义分析距离的上下文相关性,字母总数,短语级别的使用频率以及每对单词中相加的单词级别的使用频率。还提供了相关性和回归分析的结果,以定量描述刺激集。这些刺激的一个子集用于识别成功的概念组合Graves,Binder,Desai,Conant和Seidenberg的神经相关性(NeuroImage 53:638–646)。该刺激可以用于其他研究,也可以提供基准数据,以评估用于预测名词-名词对的意义的计算算法的有效性。电子补充材料本文的在线版本(doi:10.3758 / s13428-012-0256-3)包含补充材料,授权用户可以使用。

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