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
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Message Understanding Conference-6: a brief history
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MUC6 '95 Proceedings of the 6th conference on Message understanding
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Weakly-supervised acquisition of labeled class instances using graph random walks
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Corpus-based semantic lexicon induction with Web-based corroboration
UMSLLS '09 Proceedings of the Workshop on Unsupervised and Minimally Supervised Learning of Lexical Semantics
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Artificial Intelligence
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EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
Open information extraction for the web
Open information extraction for the web
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HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Coreference for learning to extract relations: yes, Virginia, coreference matters
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Extreme extraction: machine reading in a week
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
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EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Relation adaptation: learning to extract novel relations with minimum supervision
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Bootstrapping biomedical ontologies for scientific text using NELL
BioNLP '12 Proceedings of the 2012 Workshop on Biomedical Natural Language Processing
Active learning for relation type extension with local and global data views
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Open-class semantic lexicon induction is of great interest for current knowledge harvesting algorithms. We propose a general framework that uses patterns in bootstrapping fashion to learn open-class semantic lexicons for different kinds of relations. These patterns require seeds. To estimate the goodness (the potential yield) of new seeds, we introduce a regression model that considers the connectivity behavior of the seed during bootstrapping. The generalized regression model is evaluated on six different kinds of relations with over 10000 different seeds for English and Spanish patterns. Our approach reaches robust performance of 90% correlation coefficient with 15% error rate for any of the patterns when predicting the goodness of seeds.