Toward a unified theory of learning: multistrategy task-adaptive learning
Readings in knowledge acquisition and learning
Learning Information Extraction Rules for Semi-Structured and Free Text
Machine Learning - Special issue on natural language learning
Relational learning of pattern-match rules for information extraction
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
An Adaptable IE System to New Domains
Applied Intelligence
Learning Logical Definitions from Relations
Machine Learning
Machine learning for information extraction in informal domains
Machine learning for information extraction in informal domains
Word sense disambiguation using Conceptual Density
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
Learning IE rules for a set of related concepts
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
Improved heterogeneous distance functions
Journal of Artificial Intelligence Research
CRYSTAL inducing a conceptual dictionary
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
A Tool for Extension and Restructuring Natural Language Question Answering Domains
IEA/AIE '02 Proceedings of the 15th international conference on Industrial and engineering applications of artificial intelligence and expert systems: developments in applied artificial intelligence
Learning rules for information extraction
Natural Language Engineering
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The growing availability of online text has lead to an increase in the use of automatic knowledge acquisition approaches from textual data, as in Information Extraction (IE). Some IE systems use knowledge learned by single-concept learning systems, as sets of IE rules. Most of such systems need both sets of positive and negative examples. However, the manual selection of positive examples can be a very hard task for experts, while automatic methods for selecting negative examples can generate extremely large example sets, in spite of the fact that only a small subset of them is relevant to learn. This paper briefly describes a more portable multi-concept learning system and presents a methodology to select a relevant set of training examples.