Over the last two decades, numerous algorithms have been developed that successfully capture something of the semantics of single words by looking at their distribution in text and comparing these distributions in a vector space model. However, it is not straightforward to construct meaning representations beyond the level of individual words – i.e. the combination of words into larger units – using distributional methods. Our contribution is twofold. First of all, we carry out a large scale evaluation, comparing different composition methods within the distributional framework for the cases of both adjective noun and noun-noun composition, making use of a newly developed dataset. Secondly, we propose a novel method for composition, which is a generalization of lexical functions. The performance of our novel method is also evaluated on our new dataset and proves competitive with the best methods