Modularity in Knowledge Representation and Natural Language Understanding

Language ◽  
1990 ◽  
Vol 66 (4) ◽  
pp. 867
Author(s):  
Bradley L. Pritchett ◽  
Jay L. Garfield
Keyword(s):  
Knowledge Representation ◽  
Natural Language ◽  
Natural Language Understanding ◽  
Language Understanding

Guru

2012 ◽  
pp. 156-171 ◽  
Author(s):  
Andrew M. Olney ◽  
Natalie K. Person ◽  
Arthur C. Graesser
Keyword(s):  
Natural Language Processing ◽  
Knowledge Representation ◽  
Natural Language ◽  
Language Processing ◽  
Natural Language Understanding ◽  
Natural Language Generation ◽  
Language Understanding ◽  
Language Generation ◽  
Processing Techniques

The authors discuss Guru, a conversational expert ITS. Guru is designed to mimic expert human tutors using advanced applied natural language processing techniques including natural language understanding, knowledge representation, and natural language generation.

Research in knowledge representation for natural language understanding

1982 ◽  
pp. 30-31
Author(s):  
Madeleine Bates ◽  
Robert J. Bobrow ◽  
Brad Goodman ◽  
David J. Israel ◽  
Jim Schmolze ◽  
...  
Keyword(s):  
Knowledge Representation ◽  
Natural Language ◽  
Natural Language Understanding ◽  
Language Understanding

scholarly journals A Lexical Knowledge Representation Model for Natural Language Understanding

2009 ◽  
Vol 1 (4) ◽  
pp. 17-35 ◽  
Author(s):  
Ping Chen ◽  
Wei Ding ◽  
Chengmin Ding
Keyword(s):  
Knowledge Representation ◽  
Natural Language ◽  
Language Processing ◽  
Large Scale ◽  
Natural Language Understanding ◽  
Commonsense Reasoning ◽  
Lexical Knowledge ◽  
Language Understanding ◽  
Representation Model ◽  
Intelligent Information

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 article 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.

scholarly journals A Lexical Knowledge Representation Model for Natural Language Understanding

2012 ◽  
pp. 193-210
Author(s):  
Ping Chen ◽  
Wei Ding ◽  
Chengmin Ding
Keyword(s):  
Knowledge Representation ◽  
Natural Language ◽  
Language Processing ◽  
Large Scale ◽  
Natural Language Understanding ◽  
Commonsense Reasoning ◽  
Language Understanding ◽  
Representation Model ◽  
Intelligent Information ◽  
Representation Techniques

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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