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      Size sound symbolism in the English lexicon

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      Glossa: a journal of general linguistics
      Ubiquity Press, Ltd.

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          Abstract

          Experimental and cross-linguistic evidence suggests that certain speech sounds are associated with size, especially high front vowels with ‘small’ and low back vowels with ‘large’. However, empirical evidence that speech sounds are statistically associated with magnitude across words within a language has been mixed and open to methodological critique. Here, we used a random-forest analysis of a near-exhaustive set of English size adjectives (e.g.,tiny, gargantuan) to determine whether the English lexicon is characterized by size-symbolic patterns. We show that sound structure is highly predictive of semantic size in size adjectives, most strongly for the phonemes /ɪ/, /i/, /ɑ/, and /t/. In comparison, an analysis of a much larger set of more than 2,500 general vocabulary words rated for size finds no evidence for size sound symbolism, thereby suggesting that size sound symbolism is restricted to size adjectives. Our findings are the first demonstration that size sound symbolism is a statistical property of the English lexicon.

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              An introduction to recursive partitioning: rationale, application, and characteristics of classification and regression trees, bagging, and random forests.

              Recursive partitioning methods have become popular and widely used tools for nonparametric regression and classification in many scientific fields. Especially random forests, which can deal with large numbers of predictor variables even in the presence of complex interactions, have been applied successfully in genetics, clinical medicine, and bioinformatics within the past few years. High-dimensional problems are common not only in genetics, but also in some areas of psychological research, where only a few subjects can be measured because of time or cost constraints, yet a large amount of data is generated for each subject. Random forests have been shown to achieve a high prediction accuracy in such applications and to provide descriptive variable importance measures reflecting the impact of each variable in both main effects and interactions. The aim of this work is to introduce the principles of the standard recursive partitioning methods as well as recent methodological improvements, to illustrate their usage for low and high-dimensional data exploration, but also to point out limitations of the methods and potential pitfalls in their practical application. Application of the methods is illustrated with freely available implementations in the R system for statistical computing. (c) 2009 APA, all rights reserved.
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                Author and article information

                Contributors
                Journal
                Glossa: a journal of general linguistics
                Ubiquity Press, Ltd.
                2397-1835
                June 22 2021
                June 22 2021
                2021
                June 22 2021
                June 22 2021
                2021
                : 6
                : 1
                Article
                10.5334/gjgl.1646
                c4111f32-2f8c-42b7-a232-435adfb0edc9
                © 2021
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