Multilingual Harvesting of Cross-Cultural Stereotypes

People rarely articulate explicitly what a native speaker of a language is already assumed to know. So to acquire the stereotypical knowledge that underpins much of what is said in a given culture, one must look to what is implied by language rather than what is overtly stated. Similes are a convenient vehicle for this kind of knowledge, insofar as they mark out the most salient aspects of the most frequently evoked concepts. In this paper we perform a multilingual exploration of the space of common-place similes, by mining a large body of Chinese similes from the web and comparing these to the English similes harvested by Veale and Hao (2007). We demonstrate that while the simile-frame is inherently leaky in both languages, a multilingual analysis allows us to filter much of the noise that otherwise hinders the knowledge extraction process. In doing so, we can also identify a core set of stereotypical descriptions that exist in both languages and accurately map these descriptions onto a multilingual lexical ontology like HowNet. Finally, we demonstrate that conceptual descriptions that are derived from common-place similes are extremely compact and predictive of ontological structure.