Conceptual structures: information processing in mind and machine
Conceptual structures: information processing in mind and machine
A tractable machine dictionary as a resource for computational semantics
Computational lexicography for natural language processing
Semantic distance in conceptual graphs
Conceptual structures
Class-based n-gram models of natural language
Computational Linguistics
An Exploration into Semantic Distance
Proceedings of the 7th Annual Workshop on Conceptual Structures: Theory and Implementation
Concept clustering and knowledge integration from a children's dictionary
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
Using information content to evaluate semantic similarity in a taxonomy
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Expanding the Type Hierarchy with Nonlexical Concepts
AI '00 Proceedings of the 13th Biennial Conference of the Canadian Society on Computational Studies of Intelligence: Advances in Artificial Intelligence
| Hi-index | 0.00 |
Redundancy is a good thing, at least in a learning process. To be a good teacher you must say what you are going to say, say it, then say what you have just said. Well, three times is better than one. To acquire and learn knowledge from text for building a lexical knowledge base, we need to find a source of information that states facts, and repeats them a few times using slightly different sentence structures. A technique is needed for gathering information from that source and identify the redundant information. The extraction of the commonality is an active learning of the knowledge expressed. The proposed research is based on a clustering method developed by Barrière and Popowich (1996) which performs a gathering of related information about a particular topic. Individual pieces of information are represented via the Conceptual Graph (CG) formalism and the result of the clustering is a large CG embedding all individual graphs. In the present paper, we suggest that the identification of the redundant information within the resulting graph is very useful for disambiguation of the original information at the semantic level.