  This paper investigates the effects of data size and frequency range on
distributional semantic models. We compare the performance of a number of
representative models for several test settings over data of varying sizes, and
over test items of various frequency. Our results show that neural
network-based models underperform when the data is small, and that the most
reliable model over data of varying sizes and frequency ranges is the inverted
factorized model.
