Stacked Ensembles of Information Extractors for Knowledge-Base Population by Combining Supervised and Unsupervised Approaches

The UTAustin team participated in two main tasks this year the Cold Start Slot Filling (CSSF) task and the Slot-Filler Validation/Ensembling task, which was divided into the filtering and ensembling subtasks. Our system uses stacking to ensemble multiple systems for the KBP slot filling task, as described in our ACL 2015 paper. We expand the stacking approach by allowing the classifier to also utilize additions features that are relevant to making a final decision. Stacking relies on supervised training and hence requires common systems from the 2014 data to be used as training. However, that approach has limitations on performance and therefore we propose a novel approach of combining the supervised approach with an unsupervised approach on the remaining systems. We believe this combination approach gives our best run for the ensembling task. In this paper, we also discuss strategies to handle Cold Start data which comes from multiple hops.