Learning for Text Categorization and Information Extraction with ILP

Text Categorization (TC) and Information Extraction (IE) are two important goals of Natural Language Processing. While handcrafting rules for both tasks has a long tradition, learning approaches gained much interest in the past. In the present paper we try to provide a solid basis for the application of ILP methods to these learning problems. We propose to introduce three basic types (namely a type for text, one for words and one for text positions) and three simple predicate deenitions over these types which enable to write text categorization and information extraction rules as logic programs. Based on the proposed representation, we present the key concepts of our approach to the problem of learning rules for TC and IE in terms of ILP. We conclude the paper by comparing our approach of representing texts and rules as logic programs to others.