Most computational approaches to metaphor have focused on discerning between metaphorical and literal text. Recent work on computational metaphor identification (CMI) instead seeks to identify overarching conceptual metaphors by mapping selectional preferences between source and target corpora. This paper explores using semantic role labeling (SRL) in CMI. Its goals are two-fold: first, to demonstrate that semantic roles can effectively be used to identify conceptual metaphors, and second, to compare SRL to the current use of typed dependency parsing in CMI. The results show that SRL can be used to identify potential metaphors and that it overcomes some of the limitations of using typed dependencies, but also that SRL introduces its own set of complications. The paper concludes by suggesting future directions, both for evaluating the use of SRL in CMI, and for fostering critical and creative thinking about metaphors.
REF used semantic role labels and typed dependency parsing in an attempt towards computational metaphor identification.
Comparing Semantic Role Labeling with Typed Dependency Parsing in Computational Metaphor Identification
Proceedings of the NAACL HLT 2010 Second Workshop on Computational Approaches to Linguistic Creativity
Computer Science