Models of Metaphor in NLP

Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 688–697, Uppsala, Sweden, 11-16 July 2010. c⃝2010 Association for Computational Linguistics Models of Metaphor in NLP Ekaterina Shutova Computer Laboratory University of Cambridge 15 JJ Thomson Avenue Cambridge CB3 0FD, UK Ekaterina.Shutova@cl.cam.ac.uk Abstract Automatic processing of metaphor can be clearly divided into two subtasks: metaphor recognition (distinguishing be- tween literal and metaphorical language in a text) and metaphor interpretation (iden- tifying the intended literal meaning of a metaphorical expression). Both of them have been repeatedly addressed in NLP. This paper is the first comprehensive and systematic review of the existing compu- tational models of metaphor, the issues of metaphor annotation in corpora and the available resources. 1 Introduction Our production and comprehension of language is a multi-layered computational process. Hu- mans carry out high-level semantic tasks effort- lessly by subconsciously employing a vast inven- tory of complex linguistic devices, while simulta- neously integrating their background knowledge, to reason about reality. An ideal model of lan- guage understanding would also be capable of per- forming such high-level semantic tasks. However, a great deal of NLP research to date focuses on processing lower-level linguistic infor- mation, such as e.g. part-of-speech tagging, dis- covering syntactic structure of a sentence (pars- ing), coreference resolution, named entity recog- nition and many others. Another cohort of re- searchers set the goal of improving application- based statistical inference (e.g. for recognizing textual entailment or automatic summarization). In contrast, there have been fewer attempts to bring the state-of-the-art NLP technologies to- gether to model the way humans use language to frame high-level reasoning processes, such as for example, creative thought. The majority of computational approaches to figurative language still exploit the ideas articu- lated three decades ago (Wilks, 1978; Lakoff and Johnson, 1980; Fass, 1991) and often rely on task- specific hand-coded knowledge. However, recent work on lexical semantics and lexical acquisition techniques opens many new avenues for creation of fully automated models for recognition and in- terpretation of figurative language. In this pa- per I will focus on the phenomenon of metaphor and describe the most prominent computational approaches to metaphor, as well the issues of re- source creation and metaphor annotation. Metaphors arise when one concept is viewed in terms of the properties of the other. In other words it is based on similarity between the con- cepts. Similarity is a kind of association implying the presence of characteristics in common. Here are some examples of metaphor. (1) Hillary brushed aside the accusations. (2) How can I kill a process? (Martin, 1988) (3) I invested myself fully in this relationship. (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 example (2) the computa- tional process is viewed as something alive and, therefore, its forced termination is associated with the act of killing. Metaphorical expressions represent a great vari- ety, ranging from conventional …

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