Distant supervision is less expensive to obtain than directly supervised labels, but produces noisy training data whenever matching errors occur. Hence distant supervision is often coupled with learning methods that allow for this sort of noise, e.g., by introducing latent variables for each entity mention<cite> (Hoffmann et al., 2011</cite>; Riedel et al., 2010; Surdeanu et al., 2012) ; by carefully selecting the entity mentions from contexts likely to include specific KB facts (Wu and Weld, 2010) ; or by careful filtering of the KB strings used as seeds (Movshovitz-Attias and Cohen, 2012) .