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Found 6 papers, 0 papers with code
Overcoming challenges of translating deep-learning models for glioblastoma: the ZGBM consortium
Diffusion MRI is the most common non-structural imaging type, performed at every site.
Combining keyphrase extraction and lexical diversity to characterize ideas in publication titles
We compare the performance of several phrase detection models, analyze the keyphrase sets output of each, and calculate lexical diversity of corpora variants incorporating keyphrases from each model, using four common lexical diversity metrics.
Diachronic Text Mining Investigation of Therapeutic Candidates for COVID-19
In this paper we apply short-term diachronic text mining to a rapidly growing corpus of scientific publications on COVID-19 captured in the CORD-19 dataset in order to identify co-occurrences and analyze the behavior of potential candidate treatments.
Human-in-the-Loop Refinement of Word Embeddings
Word embeddings are a fixed, distributional representation of the context of words in a corpus learned from word co-occurrences.
Tracking Short-Term Temporal Linguistic Dynamics to Characterize Candidate Therapeutics for COVID-19 in the CORD-19 Corpus
We examined the intersection of a set of candidate therapeutics identified in a drug-repurposing study with temporal instances of the CORD-19 corpus to determine if it was possible to find and measure changes associated with them over time.
Interactive Re-Fitting as a Technique for Improving Word Embeddings
Word embeddings are a fixed, distributional representation of the context of words in a corpus learned from word co-occurrences.
