Mining Soft-Matching Rules from Textual Data

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.

• Type: Conference
• Year: 2001
• Where: IJCAI
• Authors: Un Yong Nahm, Raymond J. Mooney
• Keywords: IJCAI 2001, IJCAI 2007, Textual Data, Traditional Logical Rules, Unstructured Textual Data