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Dissertation/Thesis Abstract

Language Learning Through Comparison
by Babarsad, Omid Bakhshandeh, Ph.D., University of Rochester, 2017, 116; 10618060
Abstract (Summary)

Natural Language Understanding (NLU) has been one of the longest-running and the most challenging areas in artificial intelligence. For any natural language comprehension system having a basic understanding of entities and concepts is a primary requirement. Comparison, where we name the similarities and differences between entities and concepts, is a unique cognitive ability in humans which requires memorizing facts, experiencing things and integration of concepts of the world. Clearly, developing NLU systems that are capable of comprehending comparison is a crucial step forward in AI. In this thesis, I will present my research on developing systems that are capable of comprehending comparison, through which, systems can learn world knowledge and perform basic commonsense reasoning.

Indexing (document details)
Advisor: Allen, James F.
Commitee: Carlson, Gregory, Gildea, Daniel, Schubert, Lenhart
School: University of Rochester
Department: Engineering and Applied Sciences
School Location: United States -- New York
Source: DAI-B 79/02(E), Dissertation Abstracts International
Subjects: Artificial intelligence, Computer science
Keywords: Computational linguistics, Computational semantics, Knowledge acquisition, Natural language processing
Publication Number: 10618060
ISBN: 978-0-355-24157-0
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