Deep generation of metaphors
| Autor: | Simon Mille, Andrew Gargett, John A. Barnden |
|---|---|
| Rok vydání: | 2015 |
| Předmět: |
Cognitive science
Computer science business.industry Metaphor media_common.quotation_subject Natural language generation Redress computer.software_genre Newspaper Corpus linguistics Natural (music) Conversation Artificial intelligence business computer Natural language Natural language processing media_common |
| Zdroj: | TAAI |
| DOI: | 10.1109/taai.2015.7407111 |
| Popis: | We report here on progress toward a pipeline for the deep generation of metaphorical expressions in natural language. Our approach uses a combination of artificial intelligence and deep natural language generation. Metaphor is ubiquitous in forms of everyday discourse [1], [2], such as ordinary conversation, news articles, popular novels, advertisements, etc. Metaphor is an important resource for clearly and economically conveying ideas of prime human interest, such as relationships, money, disease, states of mind, passage of time. Since most Artificial Intelligence (AI) research has been about understanding rather than generating metaphorical language, such ubiquity presents a challenge to those working toward improving the ways in which AI systems understand inter-human discourse (e.g. newspaper articles, etc), or produce more natural-seeming language. Recently, there has been a renewed interest in generation, but accounts of metaphor understanding are still relatively more advanced. To redress the balance towards generation of metaphor, we directly tackle the role of AI systems in communication, uniquely combining this with corpus linguistics, deep generation and other natural language processing techniques, in order to guide output toward more natural forms of expression. |
| Databáze: | OpenAIRE |
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