This article presents Wor(l)d-GAN, a method to perform data-driven procedural content generation via machine learning in Minecraft from a single example. Based on a 3-D generative adversarial network (GAN) architecture, we are able to create arbitrarily sized world snippets from a given sample. Our method applies dense representations used in natural language processing in two ways. First, we propose block2vec representations based on word2vec. Second, we use the pretrained large language model bidirectional encoder representations from transformers (BERT) to generate representations directly from the token names. These representations make Wor(l)d-GAN independent of the number of different blocks, which can vary a lot in Minecraft, and enable the generation of larger levels. We evaluate our approach on creations from the community as well as structures generated with the Minecraft World Generator under several metrics. Wor(l)d-GAN enables its users to generate Minecraft worlds based on parts of their creations.