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1.
  • Majid, Asifa, et al. (författare)
  • Differential coding of perception in the world's languages
  • 2018
  • Ingår i: Proceedings of the National Academy of Sciences of the United States of America. - : Proceedings of the National Academy of Sciences. - 0027-8424. ; 115:45, s. 11369-11376
  • Tidskriftsartikel (refereegranskat)abstract
    • Is there a universal hierarchy of the senses, such that some senses (e.g., vision) are more accessible to consciousness and linguistic description than others (e.g., smell)? The long-standing presumption in Western thought has been that vision and audition are more objective than the other senses, serving as the basis of knowledge and understanding, whereas touch, taste, and smell are crude and of little value. This predicts that humans ought to be better at communicating about sight and hearing than the other senses, and decades of work based on English and related languages certainly suggests this is true. However, how well does this reflect the diversity of languages and communities worldwide? To test whether there is a universal hierarchy of the senses, stimuli from the five basic senses were used to elicit descriptions in 20 diverse languages, including 3 unrelated sign languages. We found that languages differ fundamentally in which sensory domains they linguistically code systematically, and how they do so. The tendency for better coding in some domains can be explained in part by cultural preoccupations. Although languages seem free to elaborate specific sensory domains, some general tendencies emerge: for example, with some exceptions, smell is poorly coded. The surprise is that, despite the gradual phylogenetic accumulation of the senses, and the imbalances in the neural tissue dedicated to them, no single hierarchy of the senses imposes itself upon language.
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2.
  • Tang, Marc (författare)
  • A typology of classifiers and gender : From description to computation
  • 2019
  • Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
    • Categorization is one the most relevant tasks realized by humans during their life, as we consistently need to categorize the things and experience that we encounter. Such need is reflected in language via various mechanisms, the most prominent being nominal classification systems (e.g., grammatical gender such as the masculine/feminine distinction in French). Typological methods are used to investigate the underlying functions and structures of such systems, using a wide variety of cross-linguistic data to examine universality and variability. This analysis is itself a classification task, as languages are categorized and clustered according to their grammatical features. This thesis provides a cross-linguistic typological analysis of nominal classification systems and in parallel compares a number of quantitative methods that can be applied at different scales.First, this thesis provides an analysis of nominal classification systems (i.e., gender and classifiers) via the description of three languages with respectively gender, classifiers, and both. While the analysis of the first two languages are more of a descriptive nature and aligns with findings in the existing literature, the third language provides novel insights to the typology of nominal classification systems by demonstrating how classifiers and gender may co-occur in one language in terms of distribution of functions. Second, the underlying logic of nominal classification systems is commonly considered difficult to investigate, e.g., is there a consistent logic behind gender assignment in language? is it possible to explain the distribution of classifier languages of the world while taking into account geographical and genealogical effects? This thesis addresses the lack of arbitrariness of nominal classification systems at three different scales: The distribution of classifiers at the worldwide level, the presence of gender within a language family, and gender assignment at the language-internal level. The methods of random forests, phylogenetics, and word embeddings with neural networks are selected since they are respectively applicable at three different scales of research questions (worldwide, family-internal, language-internal).
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