Word Meanings across Languages Support Efficient Communication
This chapter proposes that systems of semantic categories in the world's languages reflect the need for efficient communication, in that they near-optimally balance the competing principles of simplicity and informativeness. It first briefly reviews existing work that is relevant to the proposal. Next, it develops a general-purpose computational framework that instantiates the proposal, and applies it to three domains (a) color, (b) kinship, and (c) a domain in which objects are represented as binary feature vectors with qualitatively different structures. The analyses of color and kinship have shown that the framework accounts for cross-language data in both of these domains. The analysis of a domain defined in terms of binary feature vectors has provided further evidence for the generality of the framework. The chapter concludes from these three analyses that the tradeoff between simplicity and informativeness may provide a domain-general foundation for variation in category systems across languages.