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Exemplar by feature applicability matrices and other Dutch normative data for semantic concepts

机译:通过特征适用性矩阵和其他用于语义概念的荷兰标准数据进行示例

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摘要

Features are at the core of many empirical and modeling endeavors in the study of semantic concepts. This article is concerned with the delineation of features that are important in natural language concepts and the use of these features in the study of semantic concept representation. The results of a feature generation task in which the exemplars and labels of 15 semantic categories served as cues are described. The importance of the generated features was assessed by tallying the frequency with which they were generated and by obtaining judgments of their relevance. The generated attributes also featured in extensive exemplar by feature applicability matrices covering the 15 different categories, as well as two large semantic domains (that of animals and artifacts). For all exemplars of the 15 semantic categories, typicality ratings, goodness ratings, goodness rank order, generation frequency, exemplar associative strength, category associative strength, estimated age of acquisition, word frequency, familiarity ratings, imageability ratings, and pairwise similarity ratings are described as well. By making these data easily available to other researchers in the field, we hope to provide ample opportunities for continued investigations into the nature of semantic concept representation. These data may be downloaded from the Psychonomic Society's Archive of Norms, Stimuli, and Data, www.psychonomic.org/archive.
机译:特征是语义概念研究中许多经验和建模工作的核心。本文关注自然语言概念中重要特征的描述以及这些特征在语义概念表示研究中的使用。描述了特征生成任务的结果,其中15个语义类别的示例和标签用作提示。通过计算生成特征的频率并获得其相关性的判断,可以评估生成特征的重要性。生成的属性还具有广泛的示例性特征,涵盖了15个不同类别的特征适用性矩阵以及两个大型语义域(动物和人工制品的语义域)。对于15个语义类别的所有示例,描述了典型性等级,善良等级,善良等级顺序,生成频率,示例关联强度,类别关联强度,估计的获取时间,单词频率,熟悉等级,可成像性等级和成对相似性等级也一样通过使这些数据易于本领域的其他研究人员获得,我们希望为继续研究语义概念表示的性质提供充足的机会。这些数据可以从心理学会规范,刺激和数据档案库(www.psychonomic.org/archive)下载。

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