Rule discovery from textual data based on key phrase patterns
Shigeaki Sakurai, Akihiro Suyama · 2004
This paper proposes a new method for discovering rules from textual data. The method decomposes textual data into word sets by using lexical analysis, generates training examples from both key phrase relations extracted from the word sets by using key phrase patterns and text classes given by the user, and acquires key phrase relation rules from the examples by using a fuzzy inductive learning algorithm. The method is also able to deal with textual data that requires word segmentation, such as Japanese text. This paper reports on the application of the method to e-mail analysis tasks for a customer center. The e-mails are written in Japanese and have two analytical criteria: a product criterion and a contents criterion. We evaluate the acquired rules in each criterion.