  We introduce a way to represent word pairs instantiating arbitrary semantic
relations that keeps track of the contexts in which the words in the pair occur
both together and independently. The resulting features are of sufficient
generality to allow us, with the help of a standard supervised machine learning
algorithm, to tackle a variety of unrelated semantic tasks with good results
and almost no task-specific tailoring.
