  Building meaningful representations of noun compounds is not trivial since
many of them scarcely appear in the corpus. To that end, composition functions
approximate the distributional representation of a noun compound by combining
its constituent distributional vectors. In the more general case, phrase
embeddings have been trained by minimizing the distance between the vectors
representing paraphrases. We compare various types of noun compound
representations, including distributional, compositional, and paraphrase-based
representations, through a series of tasks and analyses, and with an extensive
number of underlying word embeddings. We find that indeed, in most cases,
composition functions produce higher quality representations than
distributional ones, and they improve with computational power. No single
function performs best in all scenarios, suggesting that a joint training
objective may produce improved representations.
