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Fixed Verse Generation using Neural Word Embeddings

Abstract 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. The first layer uses a lexicon based method to classify poems into types based on form and structure, and the second layer uses a supervised classification method to classify poems into subtypes based on content with an accuracy of 92%. The system th... (more)
Created Date 2016
Contributor Magge Ranganatha, Arjun (Author) / Syrotiuk, Violet R (Advisor) / Baral, Chitta (Committee member) / Hogue, Cynthia (Committee member) / Bazzi, Rida (Committee member) / Arizona State University (Publisher)
Subject Computer science / Linguistics / Artificial intelligence / Computational Creativity / Linguistic Creativity / Machine Learning / Natural Language Generation / Vector Space Model
Type Masters Thesis
Extent 94 pages
Language English
Reuse Permissions All Rights Reserved
Note Masters Thesis Computer Science 2016
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis