Rule induction with CN2: some recent improvements
EWSL-91 Proceedings of the European working session on learning on Machine learning
Separate-and-Conquer Rule Learning
Artificial Intelligence Review
Learning Information Extraction Rules for Semi-Structured and Free Text
Machine Learning - Special issue on natural language learning
Learning information extraction patterns from examples
Connectionist, Statistical, and Symbolic Approaches to Learning for Natural Language Processing
Toward general-purpose learning for information extraction
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Automatically generating extraction patterns from untagged text
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Improving learning by choosing examples intelligently in two natural language tasks
Learning language in logic
Learning Recursive Patterns for Biomedical Information Extraction
Inductive Logic Programming
Structuring Natural Language Data by Learning Rewriting Rules
Inductive Logic Programming
Towards semantic annotation supported by dependency linguistics and ILP
ISWC'10 Proceedings of the 9th international semantic web conference on The semantic web - Volume Part II
A model-driven approach of ontological components for on- line semantic web information retrieval
Journal of Web Engineering
Combining contents and citations for scientific document classification
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Information extraction from semi-structured web documents
KSEM'06 Proceedings of the First international conference on Knowledge Science, Engineering and Management
Learning information extraction rules for protein annotation from unannotated corpora
CICLing'05 Proceedings of the 6th international conference on Computational Linguistics and Intelligent Text Processing
Tuples extraction from HTML using logic wrappers and inductive logic programming
AWIC'05 Proceedings of the Third international conference on Advances in Web Intelligence
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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 used to gain much interest in the past. Since in both tasks text as a sequence of words is of crucial importance, propositional learners have strong limitations. Although viewing learning for TC and IE as ILPproblems is obvious, most approaches rather use proprietary formalisms. In the present paper we try to provide a solid basis for the application of ILPmetho ds to these learning problems. We introduce three basic types (namely a type for text, one for words and one for positions in texts) and three simple predicate definitions over these types which enable us to write text categorization and information extraction rules as logic programs. Based on the proposed representation, we present an 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.