Supervised and Unsupervised Ensembling for Knowledge Base Population
This work addresses the challenge of improving accuracy in knowledge base population for researchers and practitioners, though it appears incremental as it builds on existing ensembling techniques.
The paper tackled the problem of ensembling multiple systems for Knowledge Base Population tasks, specifically Cold Start Slot Filling and Tri-lingual Entity Discovery and Linking, by combining supervised and unsupervised methods, resulting in outperformance of the best 2015 competition system and state-of-the-art stacking approaches.
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.