Differently, distant supervision (Mintz et al., 2009;Hoffmann et al., 2011;Surdeanu et al., 2012) is to efficiently generate relational data from plain text for unseen relations with distant supervision (DS).
it naturally brings with some defects: the resulted distantly-supervised training samples are often very noisy (shown in Figure 1), which is the main problem of impeding the performance (Roth et al., 2013).