@inproceedings{estevanell-valladares-etal-2021-knowledge,
    title = "Knowledge Discovery in {COVID}-19 Research Literature",
    author = "Estevanell-Valladares, Ernesto L.  and
      Piad-Morffis, Alejandro  and
      Estevez-Velarde, Suilan  and
      Gutierrez, Yoan  and
      Montoyo, Andres  and
      Mu{\~n}oz, Rafael  and
      Almeida-Cruz, Yudivi{\'a}n",
    editor = "Mitkov, Ruslan  and
      Angelova, Galia",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)",
    month = sep,
    year = "2021",
    address = "Held Online",
    publisher = "INCOMA Ltd.",
    url = "https://preview.aclanthology.org/ingest-swisstext/2021.ranlp-1.46/",
    pages = "402--410",
    abstract = "This paper presents the preliminary results of an ongoing project that analyzes the growing body of scientific research published around the COVID-19 pandemic. In this research, a general-purpose semantic model is used to double annotate a batch of 500 sentences that were manually selected from the CORD-19 corpus. Afterwards, a baseline text-mining pipeline is designed and evaluated via a large batch of 100,959 sentences. We present a qualitative analysis of the most interesting facts automatically extracted and highlight possible future lines of development. The preliminary results show that general-purpose semantic models are a useful tool for discovering fine-grained knowledge in large corpora of scientific documents."
}