We present a novel approach to automatic metaphor identification in unrestricted text. Starting from a small seed set of manually annotated metaphorical expressions, the system is capable of harvesting a large number of metaphors of similar syntactic structure from a corpus. Our method is distinguished from previous work in that it does not employ any hand-crafted knowledge, other than the initial seed set, but, in contrast, captures metaphoricity by means of verb and noun clustering. Being the first to employ unsupervised methods for metaphor identification, our system operates with the precision of 0.79.
For example in REF a system is presented which, starting from a small seed set of manually annotated metaphorical expressions, is capable of harvesting a large number of metaphors of similar syntactic structure from a corpus.
Metaphor Identification Using Verb and Noun Clustering
COLING
Computer Science