Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Semantics based on conceptual spaces
ICLA'11 Proceedings of the 4th Indian conference on Logic and its applications
Towards a flexible semantics: colour terms in collaborative reference tasks
SemEval '12 Proceedings of the First Joint Conference on Lexical and Computational Semantics - Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation
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The paper presents a statistical evaluation of the typological data about color naming systems across the languages of the world that have been obtained by the World Color Survey. In a first step, we discuss a principal component analysis of the categorization data that led to a small set of easily interpretable features dominant in color categorization. These features were used for a dimensionality reduction of the categorization data. Using the thus preprocessed categorization data, we proceed to show that available typological data support the hypothesis by Peter Gärdenfors that the extension of color category are convex sets in the CIELab space in all languages of the world.