@inproceedings{qasemizadeh-etal-2017-projection,
    title = "Projection Al{\'e}atoire Non-N{\'e}gative pour le Calcul de Word Embedding / Non-Negative Randomized Word Embedding",
    author = "Qasemizadeh, Behrang  and
      Kallmeyer, Laura  and
      Herbelot, Aurelie",
    editor = "Eshkol-Taravella, Iris  and
      Antoine, Jean-Yves",
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. Volume 1 - Articles longs",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://preview.aclanthology.org/fix-sig-urls/2017.jeptalnrecital-long.8/",
    pages = "109--122",
    abstract = "Non-Negative Randomized Word Embedding We propose a word embedding method which is based on a novel random projection technique. We show that weighting methods such as positive pointwise mutual information (PPMI) can be applied to our models after their construction and at a reduced dimensionality. Hence, the proposed technique can efficiently transfer words onto semantically discriminative spaces while demonstrating high computational performance, besides benefits such as ease of update and a simple mechanism for interoperability. We report the performance of our method on several tasks and show that it yields competitive results compared to neural embedding methods in monolingual corpus-based setups."
}