We explore the ability of word embeddings to capture both semantic and
morphological similarity, as affected by the different types of linguistic
properties (surface form, lemma, morphological tag) used to compose the
representation of each word. We train several models, where each uses a
different subset of these properties to compose its representations. By
evaluating the models on semantic and morphological measures, we reveal some
useful insights on the relationship between semantics and morphology