Gen-Meta: Generating metaphors by combining AI and corpus-based modeling

Gargett, Andrew and Barnden, John (2015). Gen-Meta: Generating metaphors by combining AI and corpus-based modeling. Web Intelligence, 13(2) pp. 103–114.

DOI: https://doi.org/10.3233/WEB-150313

Abstract

Metaphor is important in all sorts of mundane discourse: ordinary conversation, news articles, popular novels, advertisements, etc. Issues of prime human interest – such as relationships, money, disease, states of mind, passage of time – are often most economically and understandably conveyed through metaphor. This ubiquity of metaphor presents a challenge to how Artificial Intelligence (AI) systems not only understand inter-human discourse (e.g. newspaper articles), but also produce more natural-seeming language; however, most AI research on metaphor has been about its understanding rather than its generation. To redress the balance, we directly tackle the role of AI systems in communication, uniquely combining this with corpus-based results to guide output toward more natural forms of expression.

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About

• Item ORO ID
• 73168
• Item Type
• Journal Item
• ISSN
• 1875-9289
• Project Funding Details

• Funded Project Name	Project ID	Funding Body
  Marie Curie International Incoming Fellowship	330569	Not Set

• Keywords
• Artificial intelligence; cognitive science; natural language processing; interactive system; corpus linguistics
• Academic Unit or School
• Faculty of Wellbeing, Education and Language Studies (WELS) > Languages and Applied Linguistics > Languages
  Faculty of Wellbeing, Education and Language Studies (WELS) > Languages and Applied Linguistics
  Faculty of Wellbeing, Education and Language Studies (WELS)
• Copyright Holders
• © 2015 – IOS Press and the authors.
• Depositing User
• Andrew Gargett