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
Finding interesting rules from large sets of discovered association rules
CIKM '94 Proceedings of the third international conference on Information and knowledge management
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Mining the most interesting rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Interestingness via what is not interesting
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Data mining: concepts and techniques
Data mining: concepts and techniques
What Makes Patterns Interesting in Knowledge Discovery Systems
IEEE Transactions on Knowledge and Data Engineering
Finding Interesting Patterns Using User Expectations
IEEE Transactions on Knowledge and Data Engineering
Discovery of Multiple-Level Association Rules from Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
A Mutually Beneficial Integration of Data Mining and Information Extraction
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Using information content to evaluate semantic similarity in a taxonomy
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Mining soft-matching rules from textual data
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Mining Knowledge from Text Collections Using Automatically Generated Metadata
PAKM '02 Proceedings of the 4th International Conference on Practical Aspects of Knowledge Management
A Framework for Evaluating Knowledge-Based Interestingness of Association Rules
Fuzzy Optimization and Decision Making
Combining Information Extraction with Genetic Algorithms for Text Mining
IEEE Intelligent Systems
Web mining from competitors' websites
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
On a confidence gain measure for association rule discovery and scoring
The VLDB Journal — The International Journal on Very Large Data Bases
Identifying synonymous concepts in preparation for technology mining
Journal of Information Science
Semantic-Based Temporal Text-Rule Mining
CICLing '09 Proceedings of the 10th International Conference on Computational Linguistics and Intelligent Text Processing
Extracting semantically similar frequent patterns using ontologies
SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part II
Concept chaining utilizing meronyms in text characterization
Proceedings of the 12th ACM/IEEE-CS joint conference on Digital Libraries
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In this paper, we present a new method of estimating the novelty of rules discovered by data-mining methods using WordNet, a lexical knowledge-base of English words. We assess the novelty of a rule by the average semantic distance in a knowledge hierarchy between the words in the antecedent and the consequent of the rule - the more the average distance, more is the novelty of the rule. The novelty of rules extracted by the DiscoTEX text-mining system on Amazon.com book descriptions were evaluated by both human subjects and by our algorithm. By computing correlation coefficients between pairs of human ratings and between human and automatic ratings, we found that the automatic scoring of rules based on our novelty measure correlates with human judgments about as well as human judgments correlate with one another. @Text mining