Conceptual structures: information processing in mind and machine
Conceptual structures: information processing in mind and machine
Tools and methods for computational lexicology
Computational Linguistics - Special issue of the lexicon
Analysing the dictionary definitions
Computational lexicography for natural language processing
A tractable machine dictionary as a resource for computational semantics
Computational lexicography for natural language processing
Class-based n-gram models of natural language
Computational Linguistics
Predictable Meaning Shift: Some Linguistic Properties of Lexical Implication Rules
Proceedings of the First SIGLEX Workshop on Lexical Semantics and Knowledge Representation
Acquiring and Representing Semantic Information in a Lexical Knowledge Base
Proceedings of the First SIGLEX Workshop on Lexical Semantics and Knowledge Representation
Word association norms, mutual information, and lexicography
ACL '89 Proceedings of the 27th annual meeting on Association for Computational Linguistics
Structural patterns vs. string patterns for extracting semantic information from dictionaries
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Using information content to evaluate semantic similarity in a taxonomy
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
MindNet: acquiring and structuring semantic information from text
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Redundancy: helping semantic disambiguation
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Building and Using a Lexical Knowledge Base of Near-Synonym Differences
Computational Linguistics
Concept Extraction and Clustering for Topic Digital Library Construction
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
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Knowledge structure called Concept Clustering Knowledge Graphs (CCKGs) are introduced along with a process for their construction from a machine readable dictionary. CCKGs contain multiple concepts interrelated through multiple semantic relations together forming a semantic cluster represented by a conceptual graph. The knowledge acquisition is performed on a children's first dictionary. The concepts involved are general and typical of a daily life conversation. A collection of conceptual clusters together can form the basis of a lexical knowledge base, where each CCKG contains a limited number of highly connected words giving useful information about a particular domain or situation.