Primary semantic type labeling in monologue discourse using a hierarchical classification approach

dc.contributor.advisorKoons, Robert C.en
dc.contributor.committeeMemberAsher, Nicholas M.en
dc.contributor.committeeMemberBonevac, Daniel A.en
dc.contributor.committeeMemberJuhl, Cory F.en
dc.contributor.committeeMemberPorter, Bruce W.en
dc.creatorLarson, Erik Johnen 2009en
dc.description.abstractThe question of whether a machine can reproduce human intelligence is older than modern computation, but has received a great deal of attention since the first digital computers emerged decades ago. Language understanding, a hallmark of human intelligence, has been the focus of a great deal of work in Artificial Intelligence (AI). In 1950, mathematician Alan Turing proposed a kind of game, or test, to evaluate the intelligence of a machine by assessing its ability to understand written natural language. But nearly sixty years after Turing proposed his test of machine intelligence—pose questions to a machine and a person without seeing either, and try to determine which is the machine—no system has passed the Turing Test, and the question of whether a machine can understand natural language cannot yet be answered. The present investigation is, firstly, an attempt to advance the state of the art in natural language understanding by building a machine whose input is English natural language and whose output is a set of assertions that represent answers to certain questions posed about the content of the input. The machine we explore here, in other words, should pass a simplified version of the Turing Test and by doing so help clarify and expand on our understanding of the machine intelligence. Toward this goal, we explore a constraint framework for partial solutions to the Turing Test, propose a problem whose solution would constitute a significant advance in natural language processing, and design and implement a system adequate for addressing the problem proposed. The fully implemented system finds primary specific events and their locations in monologue discourse using a hierarchical classification approach, and as such provides answers to questions of central importance in the interpretation of discourse.en
dc.subjectMachine learningen
dc.subjectHierarchical classificationen
dc.subjectNatural language processingen
dc.subjectDiscourse interpretationen
dc.titlePrimary semantic type labeling in monologue discourse using a hierarchical classification approachen
dc.type.genrethesisen University of Texas at Austinen of Philosophyen
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