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
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Two dimensional generalization in 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
Learning dictionaries for information extraction by multi-level bootstrapping
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
Machine Learning
An Adaptable IE System to New Domains
Applied Intelligence
Hierarchical Wrapper Induction for Semistructured Information Sources
Autonomous Agents and Multi-Agent Systems
Acquisition of Linguistic Patterns for Knowledge-Based Information Extraction
IEEE Transactions on Knowledge and Data Engineering
Learning Logical Definitions from Relations
Machine Learning
Maximum Entropy Markov Models for Information Extraction and Segmentation
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Selecting a Relevant Set of Examples to Learn IE-Rules
Proceedings of the 14th International conference on Industrial and engineering applications of artificial intelligence and expert systems: engineering of intelligent systems
Relational learning techniques for natural language information extraction
Relational learning techniques for natural language information extraction
Machine learning for information extraction in informal domains
Machine learning for information extraction in informal domains
A maximum entropy approach to named entity recognition
A maximum entropy approach to named entity recognition
Scenario customization for information extraction
Scenario customization for information extraction
Robust pronoun resolution with limited knowledge
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Evaluating automated and manual acquisition of anaphora resolution strategies
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Word sense disambiguation using Conceptual Density
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
The generic information extraction system
MUC5 '93 Proceedings of the 5th conference on Message understanding
SRA: description of the SRA system as used for MUC-6
MUC6 '95 Proceedings of the 6th conference on Message understanding
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
Relational learning via propositional algorithms: an information extraction case study
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Using decision trees for conference resolution
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
CRYSTAL inducing a conceptual dictionary
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Automatically generating extraction patterns from untagged text
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Adaptive information extraction
ACM Computing Surveys (CSUR)
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The growing availability of textual sources has lead to an increase in the use of automatic knowledge acquisition approaches from textual data, as in Information Extraction (IE). Most IE systems use knowledge explicitly represented as sets of IE rules usually manually acquired. Recently, however, the acquisition of this knowledge has been faced by applying a huge variety of Machine Learning (ML) techniques. Within this framework, new problems arise in relation to the way of selecting and annotating positive examples, and sometimes negative ones, in supervised approaches, or the way of organizing unsupervised or semi-supervised approaches. This paper presents a new IE-rule learning system that deals with these training set problems and describes a set of experiments for testing this capability of the new learning approach.