Language Acquisition, Neutral Change, and Diachronic Trends in Noun Classifiers

Anonymous

Keywords: acquisition, change, neutral change, classifiers, nouns, chinese, simulation

TL;DR: A empirical study and simulation of classifier change finding acquisition is a significant driver of diachronic trends

Abstract: Languages around the world employ classifier systems as a method of semantic organization and categorization. These systems are rife with variability, violability, and amiguity, and are prone to constant change over time. We explicitly model change in classifier systems as the population-level outcome of child language acquisition over time in order to shed light on the factors that drive change to classifier systems. Our research consists of two parts: a contrastive corpus study of Cantonese and Mandarin child-directed speech to determine the role that ambiguity and homophony avoidance may play in classifier learning and change followed by a series of population-level learning simulations of an abstract classifier system. We find that acquisition without reference to ambiguity avoidance is sufficient to drive broad trends in classifier change and suggest an additional role for adults and discourse factors in classifier death.