Word sense disambiguation for free-text indexing using a massive semantic network
CIKM '93 Proceedings of the second international conference on Information and knowledge management
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Document clustering using word clusters via the information bottleneck method
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
A graph model for unsupervised lexical acquisition
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data
Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
GDClust: A Graph-Based Document Clustering Technique
ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
Introduction to Information Retrieval
Introduction to Information Retrieval
Abstracting for Dimensionality Reduction in Text Classification
International Journal of Intelligent Systems
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Language ontologies provide an avenue for automated lexical analysis that may be used to supplement existing information retrieval methods. This paper presents a method of information retrieval that takes advantage of WordNet, a lexical database, to generate paths of abstraction, and uses them as the basis for an inverted index structure to be used in the retrieval of documents from an indexed corpus. We present this method as a entree to a line of research on using ontologies to perform word-sense disambiguation and improve the precision of existing information retrieval techniques.