Learning the Dimensionality of Word Embeddings

Eric Nalisnick; Sachin Ravi

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arXiv · 1511.05392

Learning the Dimensionality of Word Embeddings

Eric Nalisnick·Sachin Ravi

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Abstract

We describe a method for learning word embeddings with data-dependent dimensionality. Our Stochastic Dimensionality Skip-Gram (SD-SG) and Stochastic Dimensionality Continuous Bag-of-Words (SD-CBOW) are nonparametric analogs of Mikolov et al.'s (2013) well-known 'word2vec' models. Vector dimensionality is made dynamic by employing techniques used by Cote & Larochelle (2016) to define an RBM with an infinite number of hidden units. We show qualitatively and quantitatively that SD-SG and SD-CBOW are competitive with their fixed-dimension counterparts while providing a distribution over embedding dimensionalities, which offers a window into how semantics distribute across dimensions.

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BibTeX RIS

Eric Nalisnick, Sachin Ravi. 2017-04-13. Learning the Dimensionality of Word Embeddings. https://arxiv.org/abs/1511.05392

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