Supervised and Unsupervised Ensembling for Knowledge Base Population

2016/04/16 by Nazneen Fatema Rajani, Rajani, Nazneen Fatema, Raymond J. Mooney +1
Computer Science · Decision Sciences · #Computation and Language (cs.CL) #Data Mining Algorithms and Applications #Data Quality and Management #FOS: Computer and information sciences #Machine Learning (cs.LG) #Topic Modeling

paper · pdf · doi:10.48550/arxiv.1604.04802

openalex publication_date 2016/04/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• A Simple Approach to Building Ensembles of Naive Bayesian Classifiers for Word Sense Disambiguation

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