Efficient top-down induction of logic programs
ACM SIGART Bulletin
Machine Learning - Special issue on inductive transfer
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Learning to construct knowledge bases from the World Wide Web
Artificial Intelligence - Special issue on Intelligent internet systems
WHIRL: a word-based information representation language
Artificial Intelligence - Special issue on Intelligent internet systems
Learning Logical Definitions from Relations
Machine Learning
Machine Learning
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Handling Missing Values when Applying Classification Models
The Journal of Machine Learning Research
Open information extraction from the web
Communications of the ACM - Surviving the data deluge
Coupled semi-supervised learning for information extraction
Proceedings of the third ACM international conference on Web search and data mining
Learning to "read between the lines" using Bayesian logic programs
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Reporting bias and knowledge acquisition
Proceedings of the 2013 workshop on Automated knowledge base construction
Hi-index | 0.00 |
In this paper, we consider the problem of inductively learning rules from specific facts extracted from texts. This problem is challenging due to two reasons. First, natural texts are radically incomplete since there are always too many facts to mention. Second, natural texts are systematically biased towards novelty and surprise, which presents an unrepresentative sample to the learner. Our solutions to these two problems are based on building a generative observation model of what is mentioned and what is extracted given what is true. We first present a Multiple-predicate Bootstrapping approach that consists of iteratively learning if-then rules based on an implicit observation model and then imputing new facts implied by the learned rules. Second, we present an iterative ensemble colearning approach, where multiple decision-trees are learned from bootstrap samples of the incomplete training data, and facts are imputed based on weighted majority.