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Linguistic similarity is multi-faceted. For instance, two words may be similar with respect to semantics, syntax, or morphology inter alia. Continuous word-embeddings have been shown to capture most of these shades of similarity to some degree. This work considers guiding word-embeddings with morphologically annotated data, a form of semisupervised learning, encouraging the vectors to encode a word's morphology, i.e., words close in the embedded space share morphological features. We extend thedoi:10.3115/v1/n15-1140 dblp:conf/naacl/CotterellS15 fatcat:7mlw2fkixjambotlpwhts77cse