A La Carte Embedding: Cheap but Effective Induction of Semantic Feature Vectors

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This paper introduces a la carte embed-ding, a simple and general alternative to the usual word2vec-based approaches for building such representations that is based upon recent theoretical results for GloVe-like embeddings. Our method relies mainly on a linear transfor-mation that is efficiently learnable using pretrained word vectors and linear regression. This transform is applicable on the fly in the future when a new text feature or rare word is encountered, even if only a single usage example is available. We introduce a new dataset showing how the a la carte method requires fewer examples of words in con-text to learn high-quality embeddings and we obtain state-of-the-art results on a nonce task and some unsupervised document classification tasks.