  We present an approach to minimally supervised relation extraction that
combines the benefits of learned representations and structured learning, and
accurately predicts sentence-level relation mentions given only
proposition-level supervision from a KB. By explicitly reasoning about missing
data during learning, our approach enables large-scale training of 1D
convolutional neural networks while mitigating the issue of label noise
inherent in distant supervision. Our approach achieves state-of-the-art results
on minimally supervised sentential relation extraction, outperforming a number
of baselines, including a competitive approach that uses the attention layer of
a purely neural model.
