Stacked Ensembles of Information Extractors for Knowledge-Base Population

By Vidhoon Viswanathan, Nazneen Rajani, Yinon Bentor, Raymond J. Mooney. 2015

44 citations recorded by the source corpus.

We present results on using stacking to ensemble multiple systems for the Knowledge Base Population English Slot Filling (KBP-ESF) task. In addition to using the output and confidence of each system as input to the stacked classifier, we also use features capturing how well the systems agree about the provenance of the information they extract. We demonstrate that our stacking approach outperforms the best system from the 2014 KBPESF competition as well as alternative ensembling methods employed in the 2014 KBP Slot Filler Validation task and several other ensembling baselines. Additionally, we demonstrate that including provenance information further increases the performance of stacking.

Subfields: Artificial Intelligence, Management Science and Operations Research

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Citations in the corpus

Stacking with Auxiliary Features for Visual Question Answering (2018, methodology): “It has previously been applied effectively to information extraction (Viswanathan et al., 2015), entity linking (Rajani and Mooney, 2016) and ImageNet object detection (Rajani and Mooney, 2017).”
Using Explanations to Improve Ensembling of Visual Question Answering Systems (2017, methodology): “It has previously been applied effectively to information extraction and entity linking [Viswanathan et al. , 2015; Rajani and Mooney, 2016; 2017].”
Combining Supervised and Unsupervised Enembles for Knowledge Base Population (2016, methodology): “3871 Ensembling approach described in (Viswanathan et al., 2015) 0.”