The literary imagination may take flight on the wings of metaphor, but hard-headed scientists are just as likely as doe-eyed poets to reach for a metaphor when the descriptive need arises. Metaphor is a pervasive aspect of every genre of text and every register of speech, and is as useful for describing the inner workings of a "black hole" (itself a metaphor) as it is the affairs of the human heart. The ubiquity of metaphor in natural language thus poses a significant challenge for Natural Language Processing (NLP) systems and their builders, who cannot afford to wait until the problems of literal language have been solved before turning their attention to figurative phenomena. This book offers a comprehensive approach to the computational treatment of metaphor and its figurative brethren—including simile, analogy, and conceptual blending—that does not shy away from their important cognitive and philosophical dimensions. Veale, Shutova, and Beigman Klebanov approach metaphor from multiple computational perspectives, providing coverage of both symbolic and statistical approaches to interpretation and paraphrase generation, while also considering key contributions from philosophy on what constitutes the "meaning" of a metaphor. This book also surveys available metaphor corpora and discusses protocols for metaphor annotation. Any reader with an interest in metaphor, from beginning researchers to seasoned scholars, will find this book to be an invaluable guide to what is a fascinating linguistic phenomenon.
Dr. Tony Veale is a computer scientist at University College Dublin, Ireland, where his research focuses on the computational modeling of creative linguistic phenomena, including metaphor, blending, simile, analogy, and verbal irony. He leads the European Commission’s PROSECCO network (PROSECCO-network.eu and @PROSECCOnetwork), an international coordination action that aims to promote the scientific exploration of computational creativity. Veale is particularly interested in the generative creativity of metaphor, and builds generative models of metaphor, simile, and blending, which are made publicly available as reusable Web services to promote the integration of figurative language-processing capabilities in third-party applications. He is the author of the 2012 monograph on computational linguistic creativity titled Exploding the Creativity Myth: The Computational Foundations of Linguistic Creativity from Bloomsbury Press, and creator of the metaphor-generating Twitterbot @MetaphorMagnet. Veale is also the founder of the educational website RobotComix.com, which promotes the philosophy and practice of computational creativity to the general public via online tutorials and free textbooks such as Hand-Made By Machines? An Illustrated Guide to Creativity in Humans and Machines.
Ekaterina Shutova is a Leverhulme Early Career Fellow at the University of Cambridge Computer Laboratory. Her research is in the area of natural language processing with a specific focus on computational semantics and figurative language processing using statistical learning. Previously, she worked at the International Computer Science Institute and the Institute for Cognitive and Brain Sciences at the University of California, Berkeley and the Department of Teoretical and Applied Linguistics at the University of Cambridge. Ekaterina received her Ph.D. in computer science from the University of Cambridge in 2011 and her doctoral dissertation concerned computational modeling of figurativelanguage.
Beata Beigman Klebanov is a Senior Research Scientist in the research and development division at Educational Testing Service in Princeton, NJ. She received her Ph.D. in computer science in 2008 and her B.S. degree in computer science in 2000 (both from The Hebrew University of Jerusalem). She received her M.S. degree (with distinction) in cognitive science from the University of Edinburgh in 2001. Before joining ETS, she was a post-doctoral fellow at the Northwestern Institute for Complex Systems and Kellogg School of Management, where she researched computational approaches to political rhetoric. Her interests include discourse modeling, analyzing argumentative and figurative language, and automated semantic and pragmatic analysis of text. At ETS, her focus is on automatically scoring content in student writing. She researches methods to analyze cohesion in student essays, as well as metaphor, topicality, personalization, use of sourced content, and sentiment, among others.
