  Revealing the implicit semantic relation between the constituents of a
noun-compound is important for many NLP applications. It has been addressed in
the literature either as a classification task to a set of pre-defined
relations or by producing free text paraphrases explicating the relations. Most
existing paraphrasing methods lack the ability to generalize, and have a hard
time interpreting infrequent or new noun-compounds. We propose a neural model
that generalizes better by representing paraphrases in a continuous space,
generalizing for both unseen noun-compounds and rare paraphrases. Our model
helps improving performance on both the noun-compound paraphrasing and
classification tasks.

Computation and Language