Corpus Based Statistical Generalization Tree in Rule Optimization

A corpus-based statistical Generalization Tree model is described to achieve rule opthnization for the information extraction task. First, the user creates specific rules for the target information from the sample articles through a training interface. Second, WordNet is applied to generalize noun entities in the specific rules. The degree of generalization is adjusted to fit the user's needs by use of the statistical Generalization Tree model FinaUy, the optimally generalized rules are applied to scan new information. The results of experiments demonstrate the applicability of our Generalization Tree method.