@inproceedings{otmakhova-etal-2020-improved,
    title = "Improved Topic Representations of Medical Documents to Assist {COVID}-19 Literature Exploration",
    author = "Otmakhova, Yulia  and
      Verspoor, Karin  and
      Baldwin, Timothy  and
      {\v{S}}uster, Simon",
    editor = "Verspoor, Karin  and
      Cohen, Kevin Bretonnel  and
      Conway, Michael  and
      de Bruijn, Berry  and
      Dredze, Mark  and
      Mihalcea, Rada  and
      Wallace, Byron",
    booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID}-19 (Part 2) at {EMNLP} 2020",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2020.nlpcovid19-2.12/",
    doi = "10.18653/v1/2020.nlpcovid19-2.12",
    abstract = "Efficient discovery and exploration of biomedical literature has grown in importance in the context of the COVID-19 pandemic, and topic-based methods such as latent Dirichlet allocation (LDA) are a useful tool for this purpose. In this study we compare traditional topic models based on word tokens with topic models based on medical concepts, and propose several ways to improve topic coherence and specificity."
}