Deep learning embeddings have been successfully used for many natural language processing problems. Embeddings are mostly computed for word forms although lots of recent papers have extended this to other linguistic units like morphemes and word sequences. In this paper, we define the concept of generalized phrase that includes conventional linguistic phrases as well as skip-bigrams. We compute embeddings for generalized phrases and show in experimental evaluations on coreference resolution and paraphrase identification that such embeddings perform better than word form embeddings.
Yin et al. REF computed embeddings for generalized phrases, including both conventional linguistic phrases and skipbigrams.
An Exploration of Embeddings for Generalized Phrases
ACL
Computer Science