A Bayesian Approach to Colour Term Semantics

A Bayesian computational model is described, which is able to account for the acquisition of the meanings of basic colour terms by children learning their first language. Examples of colours named by particular colour terms are stored in a conceptual colour space, and Bayesian inference is used to determine the extent of the extension of each colour term based upon these examples. This method can be extended to create a fuzzy set based denotation for each colour term by calculating the probability that each point in the colour space comes within the extension of each colour term. The learned categories show the prototype structure characteristic of colour terms, with there being a single best example of the category, marginal members of the category, and with intermediate colours being members of the category to a greater or lesser extent. This approach has the advantage over previous approaches to colour term semantics of being both flexible enough to account for the full range of colour term systems seen in the world's languages, while at the same time providing a precise and explicit account of how children may accomplish the task of learning colour terms. Further experiments reveal that learning is successful even when as many as fifty percent of the colour term examples presented to the model are erroneous, demonstrating that the theory is robust, and can account for the acquisition of colour terms in realistic as well as idealised situations.