Paper: Unsupervised Metaphor Identification Using Hierarchical Graph Factorization Clustering

ACL ID N13-1118
Title Unsupervised Metaphor Identification Using Hierarchical Graph Factorization Clustering
Venue Annual Conference of the North American Chapter of the Association for Computational Linguistics
Session Main Conference
Year 2013
Authors

We present a novel approach to automatic metaphor identification, that discovers both metaphorical associations and metaphorical expressions in unrestricted text. Our sys- tem first performs hierarchical graph factor- ization clustering (HGFC) of nouns and then searches the resulting graph for metaphorical connections between concepts. It then makes use of the salient features of the metaphori- cally connected clusters to identify the actual metaphorical expressions. In contrast to pre- vious work, our method is fully unsupervised. Despite this fact, it operates with an encour- aging precision (0.69) and recall (0.61). Our approach is also the first one in NLP to exploit the cognitive findings on the differences in or- ganisation of abstract and concrete concepts in the human brain.