The acquisition of semantic knowledge is paramount for any application that requires a deep understanding of natural language text. Motivated by the problem of building a noun phrase-level semantic parser and adapting it to various applications, such as machine translation and multilingual question answering, in this paper we present a domain-independent model for noun phrase semantic interpretation. We investigate the problem based on cross-linguistic evidence from a set of four Romance languages: Spanish, Italian, French, and Romanian. The focus on Romance languages is well motivated. It is generally the case that English noun phrases translate into constructions of the form "N P N" in Romance languages where, as we will show, the P (preposition) varies in ways that correlate with the semantics. Thus, based on a set of 22 semantic interpretation categories (such as PART-WHOLE, AGENT, POSSESSION) we present empirical observations regarding the distribution of these semantic categories in a cross-lingual corpus and their mapping to various syntactic constructions in English and Romance. Furthermore, given a training set of English noun phrases along with their translations in the four Romance languages, our algorithm automatically learns classification rules and applies them to unseen noun phrase instances for semantic interpretation. Experimental results are compared against a state-of-the-art model reported in the literature.