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- Genre: Academic theses
Description
Wolfgang Haas is an award-winning Austrian author known primarily for his entertaining and quirky detective novels which follow the misadventures of Simon Brenner, an Austrian private investigator. These novels are notable for their subtle and not-so-subtle critiques of contemporary Austrian society and culture, their sometimes grisly content, and their unique and colloquial use of the Austrian variety of the German language. Haas has received numerous literary awards in the German-speaking world and attributes his success to the unique way he tells his stories, rather than the stories themselves. Of the seven Brenner novels that have been published thus far, only one is available in English translation, and he remains virtually unknown in the English-speaking world. This thesis includes a brief biography of Haas and an overview of his career, an analysis of his unique writing style and the problems they pose for a translator, and an English translation of the first two chapters of the novel Silentium! (1999).
ContributorsGeisler, Paul (Author) / Gilfillan, Daniel (Thesis advisor) / Ghanem, Carla (Committee member) / Hogue, Cynthia (Committee member) / Arizona State University (Publisher)
Created2013
Description
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 then uses a two-layer neural network to generate poetry based on word similarities and word movements in a 50-dimensional vector space.
The verses generated by the system are evaluated using rhyme, rhythm, syllable counts and stress patterns. These computational features of language are considered for generating haikus, limericks and iambic pentameter verses. The generated poems are evaluated using a Turing test on both experts and non-experts. The user study finds that only 38% computer generated poems were correctly identified by nonexperts while 65% of the computer generated poems were correctly identified by experts. Although the system does not pass the Turing test, the results from the Turing test suggest an improvement of over 17% when compared to previous methods which use Turing tests to evaluate poetry generators.
The verses generated by the system are evaluated using rhyme, rhythm, syllable counts and stress patterns. These computational features of language are considered for generating haikus, limericks and iambic pentameter verses. The generated poems are evaluated using a Turing test on both experts and non-experts. The user study finds that only 38% computer generated poems were correctly identified by nonexperts while 65% of the computer generated poems were correctly identified by experts. Although the system does not pass the Turing test, the results from the Turing test suggest an improvement of over 17% when compared to previous methods which use Turing tests to evaluate poetry generators.
ContributorsMagge, Arjun (Author) / Syrotiuk, Violet R. (Thesis advisor) / Baral, Chitta (Committee member) / Hogue, Cynthia (Committee member) / Bazzi, Rida (Committee member) / Arizona State University (Publisher)
Created2016