Knowledge Discovery in COVID-19 Research LiteratureDownload PDF

Aug 12, 2020 (edited Oct 08, 2020)EMNLP 2020 Workshop NLP-COVID SubmissionReaders: Everyone
  • Keywords: semantic annotation models, entity recognition, relation extraction, corpus, conditional random fields, subject-action-target
  • TL;DR: A 500-sentences corpus annotated with general-purpose semantics to train baseline machine learning models for extracting entities and relations in COVID-related papers.
  • 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 by the researchers 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.
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