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.
|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|
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