@inproceedings{budhkar-rudzicz-2019-augmenting,
    title = "Augmenting word2vec with latent {D}irichlet allocation within a clinical application",
    author = "Budhkar, Akshay  and
      Rudzicz, Frank",
    editor = "Burstein, Jill  and
      Doran, Christy  and
      Solorio, Thamar",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/N19-1414/",
    doi = "10.18653/v1/N19-1414",
    pages = "4095--4099",
    abstract = "This paper presents three hybrid models that directly combine latent Dirichlet allocation and word embedding for distinguishing between speakers with and without Alzheimer`s disease from transcripts of picture descriptions. Two of our models get F-scores over the current state-of-the-art using automatic methods on the DementiaBank dataset."
}