Analysis of Textual Data Based on Inductive Learning Techniques

Abstract

This paper introduces knowledge discovery methods based on inductive learning techniques from textual data. The author argues three methods extracting features of the textual data. First one activates a key concept dictionary, second one does a key phrase pattern dictionary, and third one does a named entity extractor. These features are used in order to generate rules representing relationships between the features and text classes. The rules are described in the format of a fuzzy decision tree. Also, these features are used in order to acquire a classification model based on SVM (Support Vector Machine). The model can classify new textual data into the text classes with high classification accuracy. Lastly, this paper introduces two application tasks based on these methods and verifies the effect of the methods.