Two recent methods based on distributional semantic models (DSMs) have proved very successful in learning high-quality vector representations of words from large corpora: word2vec and GloVe. Once trained on a very large corpus, these algorithms produce distributed representations for words in the form of vectors. DSMs based on deep learning and neural networks have proved efficient in representing the meaning of individual words. In this paper, I assess to what extent state-of-the-art word-vector semantics can help corpus linguists annotate large datasets for semantic classes. Although word vectors suggest exciting opportunities for resolving semantic annotation issues, there is still room for improvement in terms of the representation of polysemy, homonymy, and multiword expressions.