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A Lexical Knowledge Representation Model for Natural Language Understanding
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Author(s): Ping Chen (University of Houston-Downtown, USA), Wei Ding (University of Massachusetts-Boston, USA)and Chengmin Ding (IBM Business Consulting, USA)
Copyright: 2012
Pages: 18
Source title:
Software and Intelligent Sciences: New Transdisciplinary Findings
Source Author(s)/Editor(s): Yingxu Wang (University of Calgary, Canada)
DOI: 10.4018/978-1-4666-0261-8.ch012
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Abstract
Knowledge representation is essential for semantics modeling and intelligent information processing. For decades researchers have proposed many knowledge representation techniques. However, it is a daunting problem how to capture deep semantic information effectively and support the construction of a large-scale knowledge base efficiently. This paper describes a new knowledge representation model, SenseNet, which provides semantic support for commonsense reasoning and natural language processing. SenseNet is formalized with a Hidden Markov Model. An inference algorithm is proposed to simulate human-like natural language understanding procedure. A new measurement, confidence, is introduced to facilitate the natural language understanding. The authors present a detailed case study of applying SenseNet to retrieving compensation information from company proxy filings.
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