Computational approaches to figurative language

concepts that are associated with the same source domain are often related to each other in the way we conceptualise them, but their meanings are not necessarily synonymous or even semantically close. Compare, for example, the target concepts MARRIAGE and POLITICAL REGIME. Although not directly related in semantic space, they are both metaphorically mapped to the source concept of MECHANISM (e.g. “Our marriage is not really working” or “The wheels of Stalin’s regime were well oiled and already turning”). The expectation is that the target concepts’ conceptual relatedness should manifest itself in the examples of language use, i.e. they will appear in similar lexico-syntactic environments. As a result, target concepts associated with one source domain will appear together in one cluster produced using distributional clustering. Since the identification of new metaphorical associations is based on this hypothesis I first conducted a data study on the output of a state-of-the-art noun clustering method. I analysed the clusters produced by this method in order to confirm that abstract concepts get clustered together if they are associated with the same source domain and to estimate how frequently this happens.