  Deep learning embeddings have been successfully used for many natural
language processing problems. Embeddings are mostly computed for word forms
although a number of recent papers have extended this to other linguistic units
like morphemes and phrases. In this paper, we argue that learning embeddings
for discontinuous linguistic units should also be considered. In an
experimental evaluation on coreference resolution, we show that such embeddings
perform better than word form embeddings.
