Ultradense Word Embeddings by Orthogonal Transformation

NAACL 2016 Sascha RotheSebastian EbertHinrich Schütze

Embeddings are generic representations that are useful for many NLP tasks. In this paper, we introduce DENSIFIER, a method that learns an orthogonal transformation of the embedding space that focuses the information relevant for a task in an ultradense subspace of a dimensionality that is smaller by a factor of 100 than the original space... (read more)

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