Title :
Rating the interest of rules induced from data and within texts
Author_Institution :
CNRS, Orsay, France
Abstract :
Presents an application based on an evaluation of the interestingness of the rules induced from examples using inductive text mining (ITM). The better-known deductive text mining is called information extraction, and amounts to finding instances of a predefined pattern in a set of texts. ITM looks for unknown patterns or rules to discover inside a set of texts. We mainly discuss two of the problems of ITM: building ontologies of concepts, and extracting patterns
Keywords :
belief networks; data mining; learning (artificial intelligence); deductive text mining; inductive text mining; information extraction; interestingness; ontologies building; patterns extraction; rules induction; Bayesian methods; Data mining; Delta modulation; Floods; Natural languages; Ontologies; Performance analysis; Taxonomy; Text mining;
Conference_Titel :
Database and Expert Systems Applications, 2001. Proceedings. 12th International Workshop on
Conference_Location :
Munich
Print_ISBN :
0-7695-1230-5
DOI :
10.1109/DEXA.2001.953073
