For the past three decades, the design of an effective strategy for generating poetry that matches that of a human’s creative capabilities and complexities has been an elusive goal in artificial intelligence (AI) and natural language generation (NLG) research, and among linguistic creativity researchers in particular. This thesis presents a novel approach to fixed verse poetry generation using neural word embeddings. During the course of generation, a two layered poetry classifier is developed.
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- Partial requirement for: M.S., Arizona State University, 2016Note typethesis
- Includes bibliographical references (pages 60-67)Note typebibliography
- Field of study: Computer science