The paper addresses an important challenge for the automatic processing of English written text: understanding noun compounds' semantics. Following Downing (1977) , we define noun compounds as sequences of nouns acting as a single noun, e.g., bee honey, apple cake, stem cell, etc. In our view, they are best characterised by the set of all possible paraphrasing verbs that can connect the target nouns, with associated weights, e.g., malaria mosquito can be represented as follows: carry (23), spread (16), cause (12), transmit (9), etc. These verbs are directly usable as paraphrases, and using multiple of them simultaneously yields an appealing fine-grained semantic representation. In the present paper, we describe the process of constructing such representations for 250 noun-noun compounds previously proposed in the linguistic literature by Levi (1978) . In particular, using human subjects recruited through Amazon Mechanical Turk Web Service, we create a valuable manually-annotated resource for noun compound interpretation, which we make publicly available with the hope to inspire further research in paraphrase-based noun compound interpretation. We further perform a number of experiments, including a comparison to automatically generated weight vectors, in order to assess the dataset quality and the feasibility of the idea of using paraphrasing verbs to characterise noun compounds' semantics; the results are quite promising.