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Semantic representations of words have been successfully extracted fromunlabeled corpuses using neural network models like word2vec. Theserepresentations are generally high quality and are computationally inexpensiveto train, making them popular. However, these approaches generally fail toapproximate out of vocabulary (OOV) words, a task humans can do quite easily,using word roots and context clues. This paper proposes a neural network modelthat learns high quality word representations, subword representations, andcontext clue representations jointly. Learning all three types ofrepresentations together enhances the learning of each, leading to enrichedword vectors, along with strong estimates for OOV words, via the combination ofthe corresponding context clue and subword embeddings. Our model, calledEstimator Vectors (EV), learns strong word embeddings and is competitive withstate of the art methods for OOV estimation.

Estimator Vectors: OOV Word Embeddings based on Subword and Context Clue Estimates