Extracting a Knowledge Base of Mechanisms from COVID-19 Papers

October 08, 2020 · Entered Twilight · 🏛 North American Chapter of the Association for Computational Linguistics

"No code URL or promise found in abstract"
"Code repo scraped from project page (backfill)"

Evidence collected by the PWNC Scanner

Repo contents: .gitignore, LICENSE, MANIFEST.in, README.md, __init__.py, allentune, classifier_performance.pdf, examples, figs, pytest.ini, requirements.txt, setup.py, tests

Authors Tom Hope, Aida Amini, David Wadden, Madeleine van Zuylen, Sravanthi Parasa, Eric Horvitz, Daniel Weld, Roy Schwartz, Hannaneh Hajishirzi arXiv ID 2010.03824 Category cs.CL: Computation & Language Cross-listed cs.IR, cs.LG Citations 30 Venue North American Chapter of the Association for Computational Linguistics Repository https://github.com/allenai/allentune.git ⭐ 140 Last Checked 15 days ago

Abstract

The COVID-19 pandemic has spawned a diverse body of scientific literature that is challenging to navigate, stimulating interest in automated tools to help find useful knowledge. We pursue the construction of a knowledge base (KB) of mechanisms -- a fundamental concept across the sciences encompassing activities, functions and causal relations, ranging from cellular processes to economic impacts. We extract this information from the natural language of scientific papers by developing a broad, unified schema that strikes a balance between relevance and breadth. We annotate a dataset of mechanisms with our schema and train a model to extract mechanism relations from papers. Our experiments demonstrate the utility of our KB in supporting interdisciplinary scientific search over COVID-19 literature, outperforming the prominent PubMed search in a study with clinical experts.