Abstract This study examines the ability of a semantic space model to represent the meaning of noun compounds such as “information gathering” or “weather forecast”. A new algorithm, comparison, is proposed for computing compound vectors from constituent word vectors, and compared with other algorithms (i.e., predication and centroid) in terms of accuracy of multiple-choice synonym test and similarity judgment test. The result of both tests is that the comparison algorithm is, on the whole, superior to other algorithms, and in particular achieves the best performance when noun compounds have emergent meanings. Furthermore, the comparison algorithm also works for novel noun compounds that do not occur in the corpus. These fifindings indicate that a semantic space model in general and the comparison algorithm in particular has suffificient ability to compute the meaning of noun compounds