Combining Supervised and Unsupervised Enembles for Knowledge Base Population

Nazneen Fatema Rajani1 and Raymond Mooney2
1The University of Texas at Austin, 2University of Texas at Austin


Abstract

We combine supervised and unsupervised methods to ensemble 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.