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 specific 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 limit..