  We present different methods for obtaining descriptive properties
automatically for the 151 original Pok\'emon. We train several different word
embeddings models on a crawled Pok\'emon corpus, and use them to rank
automatically English adjectives based on how characteristic they are to a
given Pok\'emon. Based on our experiments, it is better to train a model with
domain specific data than to use a pretrained model. Word2Vec produces less
noise in the results than fastText model. Furthermore, we expand the list of
properties for each Pok\'emon automatically. However, none of the methods is
spot on and there is a considerable amount of noise in the different semantic
models. Our models have been released on Zenodo.
