Combining Supervised and Unsupervised Ensembles for Knowledge Base Population (2016)
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.
To Appear In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (EMNLP-16) 2016.

Raymond J. Mooney Faculty mooney [at] cs utexas edu
Nazneen Rajani Ph.D. Student nrajani [at] cs utexas edu