Efficient Non-parametric Estimation of Multiple Embeddings per Word in Vector Space

@article{Neelakantan2014EfficientNE,
  title={Efficient Non-parametric Estimation of Multiple Embeddings per Word in Vector Space},
  author={Arvind Neelakantan and Jeevan Shankar and Alexandre Passos and Andrew McCallum},
  journal={ArXiv},
  year={2014},
  volume={abs/1504.06654}
}
There is rising interest in vector-space word embeddings and their use in NLP, especially given recent methods for their fast estimation at very large scale. [] Key Method It differs from recent related work by jointly performing word sense discrimination and embedding learning, by non-parametrically estimating the number of senses per word type, and by its efficiency and scalability. We present new state-of-the-art results in the word similarity in context task and demonstrate its scalability by training…

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