Title: Comparing semantic and morphological analogy completion in word embeddings. Abstract: Word embeddings have prompted great excitement in the NLP community due to their capacity for generalization to unforeseen tasks, including semantic analogy completion. Features such as color and category relationships have been examined by previous work, but this is the first research considering the morphological relationships encoded in word embeddings. We construct several natural experiments examining analogy completion across word stems modified by affixes, and find no evidence that Word2Vec, glove, and fasttext models encode these morphological relationships. We note that a special case of this problem is part-of-speech transformation, and note that the lack of support for part-of-speech analogies is surprising in the context of other successful cases of semantic inference using word embeddings.