A Goal-Directed Functionally-Based Stylistic Analyzer

If sophisticated natural language systems are to handle the full range of communication, then they must be able to account for the nuances and subtleties of linguistic style. A computational treatment of style would be highly advantageous to natural language understanding and generation, with particular relevance to intelligent computer-assisted language instruction and machine translation. These systems would be able to understand more complex and expressive language, produce text suitable for a speci c occasion, help a second-language learner develop a more natural and appropriate style, and produce higher quality translations of text. A foundation for AI-based computational style has been laid by DiMarco, with extensions by Green, Makuta-Giluk, Mah, and Payette in generation, rhetoric, comparative stylistics, and intelligent computer-aided language instruction, respectively. These researchers found that DiMarco's work, while an important step in computational stylistics, was limited due to the lack of a theoretical foundation. DiMarco and Hirst provided a preliminary theoretical foundation and Green extended their work. This thesis uni es these complementary, and sometimes contradictory, theories of syntactic style. A de nitive grammar of style, based on this revised theory, is developed and used to implement a stylistic analyzer, Asset. The revised theory of syntactic style and its implementation show that human-independent computer analysis of style is a feasible goal for computational linguistics. Acknowledgements There are many people who, during the time I was working on this thesis, made the task easier. They either distracted me, expressed their faith in me, went out of their way to be helpful, or gave me their support. I am grateful to all. There are four people, however, whom I want to publicly acknowledge. First, many thanks to my supervisor, Chrysanne DiMarco. Her unfailing faith in my ability, her encouragement when I needed it, and her generosity let me believe that I could get this thesis done. Devin and Logan, just by being there, were a constant reminder that there was more to life than writing a thesis. As a result, there were many times I returned, refreshed, to the task of writing. And nally there is John, without whom keeping things in perspective would have been almost impossible. I thank you. 1