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Predicting if two names refer to the same entity is an important task for many domains, such as information retrieval, record linkage and data integration. In this paper, we propose to create name-embeddings by employing a Doc2Vec methodology, where each name is viewed as a document and each letter in the name is considered a word. Our hypothesis is that representing names as documents, with letters as words, will help capture the internal structure of names and relationships among letters. We present and discuss an experimental study where we explore the effect of various parameters, and we assess the stability of the models built for the embedding of names. Our results show that the new proposed method can predict with high accuracy when a pair of names matches.