"Nut case: what does it mean?" Understanding semantic relationship between nouns in noun compounds through paraphrasing and ranking the paraphrases

A noun compound (NC) is a sequence of two or more nouns (entities) acting as a single noun entity that encodes implicit semantic relation between its noun constituents. Given an NC such as 'headache pills' and possible paraphrases such as: 'pills that induce headache' or 'pills that relieve head-ache' can we learn to choose which verb: 'induce' or 'relieve' that best describes the semantic relation encoded in 'headache pills'? In this paper, we describe our approaches to rank human-proposed paraphrasing verbs of NCs. Our contribution is a novel approach that uses two-step process of clustering similar NCs and then labeling the best paraphrasing verb as the most prototypical verb in the cluster. The approach performs the best with an average Spearman's rank correlation of 0.55. This approach, while being computationally simpler, gives a better ranking than the current state of the art. The result shows the potential of our approach for finding implicit relations between entities especially when the relations are not explicit in the context in which the entities appear, rather they are implicit in the relationship between its constituents.