Word embeddings improve generalization over lexical features by placing each word in a lower-dimensional space, using distributional information obtained from unlabeled data.However, the effectiveness of word embeddings for downstream NLP tasks is limited by out-of-vocabulary (OOV) words, for which embeddings do not exist.In this paper, we present MIM-ICK, an approach to generating OOV word embeddings compositionally, by learning a function from spellings to distributional embeddings.Unlike prior work, MIMICK does not require re-training on the original word embedding corpus; instead, learning is performed at the type level.Intrinsic and extrinsic evaluations demonstrate the power of this simple approach.On 23 languages, MIMICK improves performance over a word-based baseline for tagging part-of-speech and morphosyntactic attributes.It is competitive with (and complementary to) a supervised characterbased model in low-resource settings.

Word embeddings allow natural language processing systems to share statistical information across related words.These embeddings are typically based on distributional statistics, making it difficult for them to generalize to rare or unseen words.We propose to improve word embeddings by incorporating morphological information, capturing shared sub-word features.Unlike previous work that constructs word embeddings directly from morphemes, we combine morphological and distributional information in a unified probabilistic framework, in which the word embedding is a latent variable.The morphological information provides a prior distribution on the latent word embeddings, which in turn condition a likelihood function over an observed corpus.This approach yields improvements on intrinsic word similarity evaluations, and also in the downstream task of part-of-speech tagging.