Metaphor Identification Using Verb and Noun Clustering

Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010), pages 1002–1010, Beijing, August 2010 Metaphor Identification Using Verb and Noun Clustering Ekaterina Shutova, Lin Sun and Anna Korhonen Computer Laboratory, University of Cambridge es407,ls418,alk23@cam.ac.uk Abstract We present a novel approach to auto- matic metaphor identification in unre- stricted text. Starting from a small seed set of manually annotated metaphorical ex- pressions, the system is capable of har- vesting a large number of metaphors of similar syntactic structure from a corpus. Our method is distinguished from previ- ous 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 un- supervised methods for metaphor identifi- cation, our system operates with the pre- cision of 0.79. 1 Introduction Besides enriching our thought and communica- tion with novel imagery, the phenomenon of metaphor also plays a crucial structural role in our use of language. Metaphors arise when one con- cept is viewed in terms of the properties of the other. Below are some examples of metaphor. (1) How can I kill a process? (Martin, 1988) (2) Inflation has eaten up all my savings. (Lakoff and Johnson, 1980) (3) He shot down all of my arguments. (Lakoff and Johnson, 1980) (4) And then my heart with pleasure fills, And dances with the daffodils.1 In metaphorical expressions seemingly unrelated features of one concept are associated with an- other concept. In the computer science metaphor 1“I wandered lonely as a cloud”, William Wordsworth, 1804. in (1) the computational process is viewed as something alive and, therefore, its forced termi- nation is associated with the act of killing. Lakoff and Johnson (1980) explain metaphor as a system- atic association, or a mapping, between two con- cepts or conceptual domains: the source and the target. The metaphor in (3) exemplifies a map- ping of a concept of argument to that of war. The argument, which is the target concept, is viewed in terms of a battle (or a war), the source concept. The existence of such a link allows us to talk about arguments using the war terminology, thus giving rise to a number of metaphors. Characteristic to all areas of human activity (from poetic to ordinary to scientific) and, thus, to all types of discourse, metaphor becomes an important problem for natural language process- ing (NLP). In order to estimate the frequency of the phenomenon, Shutova and Teufel (2010) con- ducted a corpus study on a subset of the British National Corpus (BNC) (Burnard, 2007) repre- senting various genres. They manually anno- tated metaphorical expressions in this data and found that 241 out of 761 sentences contained a metaphor, whereby in 164 phrases metaphoricity was introduced by a verb. Due to such a high fre- quency of their use, a system capable of recog- nizing and interpreting metaphorical expressions in unrestricted text would become an invaluable component of any semantics-oriented NLP appli- cation. …

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