Expanding and Weighting Stereotypical Properties of Human Characters for Linguistic Creativity

Many linguistic creativity applications rely heavily on knowledge of nouns and their properties. However, such knowledge sources are scarce and limited. We present a graph-based approach for expanding and weighting properties of nouns with given initial, nonweighted properties. In this paper, we focus on famous characters, either real or fictional, and categories of people, such as Actor, Hero, Child etc. In our case study, we started with an average of 11 and 25 initial properties for characters and categories, for which the method found 63 and 132 additional properties, respectively. An empirical evaluation shows that the expanded properties and weights are consistent with human judgement. The resulting knowledge base can be utilized in creation of figurative language. For instance, metaphors based on famous characters can be used in various applications including story generation, creative writing, advertising and comic generation.