First, we focused on improving path representation using LSTM, resulting in a path-based model that performs significantly better than prior path-based methods, and matches the performance of the previously superior distributional methods.
we demonstrated that the increase in recall is a result of generalizing semantically-similar paths, in contrast to prior methods, which either make no generalizations or over-generalize paths.