Metaphors are intriguing. We know that George Lakoff and Mark Johnson, in their book Metaphors We Live By, pointed out that our daily language is full of metaphors (Lakoff and Johnson 1980) . Metaphors are not rare at all as linguistic events and in general as a means of human communication (e.g., in visual art). Thus, more than an exception, they seem to be a necessity for our mind. People use metaphors as strategies to link concepts, to deal with situations, and sometimes to suggest solutions to problems. For example, you can see criminality as a monster or as an illness. What you have in mind to deal with it is probably to fight in the former case, and to cure or to plan prevention in the latter. Nonetheless, metaphors are extremely difficult to model in a computational framework. What is literal? What is metaphorical? Making this distinction has often proved to be a daunting task, even for a human judgment. Metaphors-so bound to our way of thinking and entangled with a huge quantity of knowledge and linguistics subtletiesconstitute an excellent research problem for computational linguistics and artificial intelligence in general. We could probably say that they belong to the AI-complete problems. The difficulty of these computational problems is equivalent to that of solving the central artificial intelligence problem-making computers as intelligent as people. Tony Veale, Ekaterina Shutova, and Beata B. Klebanov are experienced researchers in the field of figurative language processing. Their book is an excellent resource and a good reference for anyone who plans to tackle the subtleties of this complex topic. The book offers a comprehensive approach to the computational treatment of metaphors and of related figurative devices such as simile, analogy, and conceptual blending. The reader is introduced to multiple computational perspectives, from symbolic and statistical approaches to interpretation and paraphrase generation, without omitting contributions from philosophy on what constitutes the significance of a metaphor. The first three chapters introduce the reader to the concept of metaphor, particularly the theoretical foundations profitable for approaching the problem computationally. Particularly useful is the explanation of the related figurative devices: similes (the comparison of one thing with another of a different kind, used to make a description more emphatic or vivid, e.g., John is as brave as a lion); analogy (a comparison between one thing and another, typically for the purpose of explanation, e.g., marriage is slavery); and conceptual blending (a cognitive theory, originally developed by Gilles Fauconnier and Mark Turner [Fauconnier and Turner 2002] , which refers to a set of cognitive operations for combining-or blending-words, images, and ideas in a network of "mental spaces" to create meaning, e.g., the painting in George Clooney's attic is a cue to create a conceptual
Generating metaphors is a challenge in artificial intelligence REF .
Metaphor: A Computational Perspective
Computational Linguistics
Computational Linguistics
Computer Science