1. A method comprising: analyzing, for a first natural language processing task, a first natural language corpus to generate a latent representation for words in the first natural language corpus; calculating, for each word in the latent representation, a Euclidian distance between a left context of the each word and a right context of the each word, to yield a centroid of latent vectors for each word in the latent representation; analyzing, for a second natural language processing task, a second natural language corpus having a target word, the target word being a word that is not in the first natural language corpus; and predicting, via a processor, a label for the target word based on the latent representation and the centroid of latent vectors for each word in the latent representation, wherein the predicting comprises iteratively executing an alternating gradient descent algorithm until convergence, the alternating gradient descent algorithm comprising, for each iteration, computing a low dimensional continuous embedding and passing the low dimensional continuous embedding through a multi-layer perceptron.