In this work, we report the results of our experiments on the task of distinguishing the semantics of verb-noun collocations in a Spanish corpus. This semantics was represented by four lexical functions of the Meaning-Text Theory. Each lexical function specifies a certain universal semantic concept found in any natural language. Knowledge of collocation and its semantic content is important for natural language processing, as collocation comprises the restrictions on how words can be used together. We experimented with a combination of GloVe word embeddings as a recent and extended algorithm for vector representation of words and a deep neural architecture, in order to recover most of the context of verb-noun collocations in a meaningful way which could discriminate among lexical functions. Our corpus was a collection of 1,131 Excelsior newspaper issues. As our results showed, the proposed deep neural architecture outperformed state-of-the-art supervised learning methods.