Text mining concerns the discovery of knowledge from unstructured textual data. One important task is the discovery of rules that relate specific words and phrases. Although existing methods for this task learn traditional logical rules, soft-matching methods that utilize word-frequency information generally work better for textual data. This paper presents a rule induction system, TextRISE, that allows for partial matching of text-valued features by combining rule-based and instance-based learning. We present initial experiments applying TextRISE to corpora of book descriptions and patent documents retrieved from the web and compare its results to those of traditional rule and instance based methods.

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In Proceedings of the 18th International Joint Conference on Artificial Intelligence 2001.

@InProceedings{nahm:ijcai01, title={Mining Soft-Matching Rules from Textual Data}, author={Un Yong Nahm and Raymond J. Mooney}, booktitle={Proceedings of the 18th International Joint Conference on Artificial Intelligence}, key={IJCAI}, url="http://www.cs.utexas.edu/users/ai-lab?nahm:ijcai01", year={2001} }