Selective Sampling Using the Query by Committee Algorithm
Machine Learning
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
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
Snowball: extracting relations from large plain-text collections
DL '00 Proceedings of the fifth ACM conference on Digital libraries
Analyzing the effectiveness and applicability of co-training
Proceedings of the ninth international conference on Information and knowledge management
Learning pattern rules for Chinese named entity extraction
Eighteenth national conference on Artificial intelligence
Counter-training in discovery of semantic patterns
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
A bootstrapping method for learning semantic lexicons using extraction pattern contexts
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Active learning with strong and weak views: a case study on wrapper induction
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
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Information extraction is becoming an important task due to the vast growth of the online texts. Pattern rule induction is one kind of main methods to do information extraction. Manually constructing pattern rules is tedious and error prone. In this paper, we present GRID_CoTrain, a weakly supervised paradigm by bootstrapping GRID (a supervised rule induction system) with cotraining and active learning. We also utilize external knowledge resource such as WordNet and existing ontology knowledge to optimize the learned pattern rules.