Extracting prototypes from exemplars What can corpus data tell us about concept representation?

Over the past four decades, two distinct alternatives have emerged to rule-based models of how linguistic categories are stored and represented as cognitive structures, namely the and theories. Although these models were initially thought to be mutually exclusive, shifts from one mechanism to the other have been observed in category learning experiments, bringing the models closer together. In this paper we implement a technique akin to varying abstraction modelling, that assumes intermediate abstraction processes to underlie category representations and categorization decisions; we do so using familiar statistical techniques such as regression and clustering that track frequency distributions in input. With this model we simulate, on the basis of actual usage of Russian verbs and Finnish verbs as observed in corpora, how prototypes for near-synonymous verbs could be formed from concrete exemplars at different levels of abstraction. In so doing, we take a closer look at the cognitive linguistic flirtation with multiple categorization theories, suggesting three improvements anchored in the fact that cognitive linguistics is a usage-based theory of language. Firstly, we show that language provides support for considering single prototype and full exemplar models as opposite ends along a continuum of abstraction. Secondly, we present a methodology that simulates how prototypes can be obtained from exemplars at more than one level of abstraction in a systematic and verifiable way. And thirdly, we illustrate our claims on the basis of work on verbs, denoting intangible events that are neither stable in nor independent of time and express relational concepts; this implies that verbs are more susceptible to their meanings being influenced by the concepts they relate.