<cite>MULTIR</cite> is based on multi-instance learning, which assumes that at least one sentence of those matching a given entity-pair contains the relation of interest (Riedel et al., 2010) in the given knowledge base to tolerate false positive noise in the training data and superior than previous models (Riedel et al., 2010; Mintz et al., 2009 ) by allowing overlapping relations. <cite>MULTIR</cite> uses features which are based on Mintz et al. (2009) and consist of conjunctions of named entity tags, syntactic dependency paths between arguments, and lexical information.