@article{osborne-etal-2016-encoding,
    title = "Encoding Prior Knowledge with Eigenword Embeddings",
    author = "Osborne, Dominique  and
      Narayan, Shashi  and
      Cohen, Shay B.",
    editor = "Lee, Lillian  and
      Johnson, Mark  and
      Toutanova, Kristina",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "4",
    year = "2016",
    address = "Cambridge, MA",
    publisher = "MIT Press",
    url = "https://preview.aclanthology.org/fix-sig-urls/Q16-1030/",
    doi = "10.1162/tacl_a_00108",
    pages = "417--430",
    abstract = "Canonical correlation analysis (CCA) is a method for reducing the dimension of data represented using two views. It has been previously used to derive word embeddings, where one view indicates a word, and the other view indicates its context. We describe a way to incorporate prior knowledge into CCA, give a theoretical justification for it, and test it by deriving word embeddings and evaluating them on a myriad of datasets."
}