We propose an algorithm that combines supervised and unsupervised methods to ensemble multiple systems for two popular Knowledge Base Population (KBP) tasks, Cold Start Slot Filling (CSSF) and Tri-lingual Entity Discovery and Linking (TEDL). We demonstrate that it outperforms the best system for both tasks in the 2015 competition, several ensembling baselines, as well as a state-of-the-art stacking approach. The success of our technique on two different and challenging problems demonstrates the power and generality of our combined approach to ensembling.

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To Appear In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (EMNLP-16) 2016.

@inproceedings{rajani:emnlp16, title={Combining Supervised and Unsupervised Ensembles for Knowledge Base Population}, author={Nazneen Fatema Rajani and Raymond J. Mooney}, booktitle={Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (EMNLP-16)}, url="http://www.cs.utexas.edu/users/ai-labpub-view.php?PubID=127566", year={2016} }