Deep Learning Embeddings for Discontinuous Linguistic Units

Wenpeng Yin, Hinrich Schütze

Dec 19, 2013 (modified: Dec 19, 2013) ICLR 2014 workshop submission readers: everyone
  • Decision: submitted, no decision
  • Abstract: Deep learning embeddings have been successfully used for many natural language processing (NLP) 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.