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
dc.date.accessioned2010-08-20T16:03:16Zen
dc.date.available2010-08-20T16:03:16Zen
dc.date.available2010-08-20T16:03:21Zen
dc.date.issued2009-12en
dc.date.submittedDecember 2009en
dc.date.updated2010-08-20T16:03:21Zen
dc.descriptiontexten
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.description.departmentPhilosophy
dc.format.mimetypeapplication/pdfen
dc.identifier.urihttp://hdl.handle.net/2152/ETD-UT-2009-12-636en
dc.language.isoengen
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
thesis.degree.departmentPhilosophyen
thesis.degree.disciplinePhilosophyen
thesis.degree.grantorThe University of Texas at Austinen
thesis.degree.levelDoctoralen
thesis.degree.nameDoctor of Philosophyen
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