Most existing methods (Yogatama et al., 2015;Ling and Weld, 2012) treat every type label in a training mention's candidate type set equally and independently when learning the classifiers but ignore the fact that types in the given hierarchy are semantically correlated (e.g., actor is more relevant to singer than to politician).
the learned classifiers may bias  Table 1: A study of label noise.