  Supervised distributional methods are applied successfully in lexical
entailment, but recent work questioned whether these methods actually learn a
relation between two words. Specifically, Levy et al. (2015) claimed that
linear classifiers learn only separate properties of each word. We suggest a
cheap and easy way to boost the performance of these methods by integrating
multiplicative features into commonly used representations. We provide an
extensive evaluation with different classifiers and evaluation setups, and
suggest a suitable evaluation setup for the task, eliminating biases existing
in previous ones.
