  We present results on combining 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 our combined system along with auxiliary
features outperforms the best performing system for both tasks in the 2015
competition, several ensembling baselines, as well as the state-of-the-art
stacking approach to ensembling KBP systems. The success of our technique on
two different and challenging problems demonstrates the power and generality of
our combined approach to ensembling.

Computation and Language;Machine Learning