We present a novel application of WordNet to estimating the interestingness of rules discovered by data-mining methods. We estimate the novelty of text-mined rules using semantic distance measures based on WordNet. In our experiments, 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 each other.

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In Proceedings of NAACL 2001 Workshop on WordNet and Other Lexical Resources: Applications, Extensions and Customizations, pp. 144--149, Pittsburg, PA, June 2001.

@inproceedings{basu:naacl01, title={Using Lexical Knowlege to Evaluate the Novelty of Rules Mined from Text}, author={Sugato Basu and Raymond J. Mooney and Krupakar V. Pasupuleti and Joydeep Ghosh}, booktitle={Proceedings of NAACL 2001 Workshop on WordNet and Other Lexical Resources: Applications, Extensions and Customizations}, month={June}, address={Pittsburg, PA}, pages={144--149}, url="http://www.cs.utexas.edu/users/ai-lab?basu:naacl01", year={2001} }