The nferX software is a cloud-based platform that enables users to dynamically query the universe of possible conceptual associations from over 100 million biomedical documents, including the COVID-19 Open Research Dataset recently announced by the White House (The White House, 2020; Figure 1). An unsupervised neural network is used to recognize and preserve complex biomedical phraseology as 300 million searchable tokens, beyond the simpler words that have generally been explored using higher dimensional word embeddings previously (Mikolov et al., 2013a). Our local context score is derived from pointwise mutual information content between pairs of these tokens and can be retrieved dynamically. Our global context score is derived using word2vec (Mikolov et al., 2013a), as the cosine similarity between 180 million word vectors projected in a 300 dimensional space (Figure 1A, Figure 1—figure supplement 1).