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

The 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. / text

Identiferoai:union.ndltd.org:UTEXAS/oai:repositories.lib.utexas.edu:2152/ETD-UT-2009-12-636
Date20 August 2010
CreatorsLarson, Erik John
ContributorsKoons, Robert C.
Source SetsUniversity of Texas
LanguageEnglish
Detected LanguageEnglish
Typethesis
Formatapplication/pdf

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