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We present an approach to minimally supervised relation extraction thatcombines the benefits of learned representations and structured learning, andaccurately predicts sentence-level relation mentions given onlyproposition-level supervision from a KB. By explicitly reasoning about missingdata during learning, our approach enables large-scale training of 1Dconvolutional neural networks while mitigating the issue of label noiseinherent in distant supervision. Our approach achieves state-of-the-art resultson minimally supervised sentential relation extraction, outperforming a numberof baselines, including a competitive approach that uses the attention layer ofa purely neural model.

Structured Minimally Supervised Learning for Neural Relation Extraction