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This study uses word embeddings to analyze reduplicative constructions in Mandarin Chinese, revealing semantic and pragmatic differentiation and validating distributional semantics for linguistic investigation.
This paper uses word2vec embeddings and neural networks to analyze the inherent fuzziness in parts of speech categorization, creating a 3D semantic space to visualize prototypical words and boundaries between linguistic categories.
This paper introduces Semantic Field Theory (SFT), a computational model for lexical semantics that models meaning through semantic fields, contextual deformation, interaction terms, and energy minimization. It provides formal elements including Gaussian product closure, Möbius inversion for higher-order interactions, and stability conditions.