We further note that iterative bootstrapping over a single distant supervision system is difficult, because state-of-the-art systems (Surdeanu et al., 2012; <cite>Hoffmann et al., 2011</cite>; Riedel et al., 2010; Mintz et al., 2009) , detect only few false negatives in the training data due to their high-precision low-recall features, which were originally proposed by Mintz et al. (2009) . We present a reliable and novel way to address these issues and achieve significant improvement over the <cite>MULTIR</cite> system (<cite>Hoffmann et al., 2011</cite>) , increasing recall from 47.7% to 61.2% at comparable precision.