Automatic acquisition of hyponyms from large text corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
K-means clustering via principal component analysis
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Exploring various knowledge in relation extraction
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Measuring the similarity between implicit semantic relations from the web
Proceedings of the 18th international conference on World wide web
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Open information extraction from the web
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Multi-task transfer learning for weakly-supervised relation extraction
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2
Relational duality: unsupervised extraction of semantic relations between entities on the web
Proceedings of the 19th international conference on World wide web
Not all seeds are equal: measuring the quality of text mining seeds
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Coupled attribute analysis on numerical data
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Extracting the relations that exist between two entities is an important step in numerous Web-related tasks such as information extraction. A supervised relation extraction system that is trained to extract a particular relation type might not accurately extract a new type of a relation for which it has not been trained. However, it is costly to create training data manually for every new relation type that one might want to extract. We propose a method to adapt an existing relation extraction system to extract new relation types with minimum supervision. Our proposed method comprises two stages: learning a lower-dimensional projection between different relations, and learning a relational classifier for the target relation type with instance sampling. We evaluate the proposed method using a dataset that contains 2000 instances for 20 different relation types. Our experimental results show that the proposed method achieves a statistically significant macro-average F-score of 62.77. Moreover, the proposed method outperforms numerous baselines and a previously proposed weakly-supervised relation extraction method.