Tailoring Word Embeddings for Bilexical Predictions: An Experimental   Comparison
  We investigate the problem of inducing word embeddings that are tailored for a particular bilexical relation. Our learning algorithm takes an existing lexical vector space and compresses it such that the resulting word embeddings are good predictors for a target bilexical relation. In experiments we show that task-specific embeddings can benefit both the quality and efficiency in lexical prediction tasks. 
Pranava Swaroop Madhyastha, Xavier Carreras, Ariadna Quattoni
cs.CL cs.LG