Distant and weak supervision allow to obtain large amounts of labeled training data quickly cheaply, but these automatic annotations tend contain a high amount errors. A popular technique overcome the negative effects noisy labels is noise modelling where underlying process modelled. In this work, we study quality estimated models from theoretical side by deriving expected error model. Apart ev...