About: Document Classification for COVID-19 Literature

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Document Classification for COVID-19 Literature
Creator	Zhang, Dongdong Zhang, Ping Su, Yu Jiménez Gutiérrez, Bernal Zeng, Juncheng
source	ArXiv
abstract	The global pandemic has made it more important than ever to quickly and accurately retrieve relevant scientific literature for effective consumption by researchers in a wide range of fields. We provide an analysis of several multi-label document classification models on the LitCovid dataset, a growing collection of 8,000 research papers regarding the novel 2019 coronavirus. We find that pre-trained language models fine-tuned on this dataset outperform all other baselines and that the BioBERT and novel Longformer models surpass all others with almost equivalent micro-F1 and accuracy scores of around 81% and 69% on the test set. We evaluate the data efficiency and generalizability of these models as essential features of any system prepared to deal with an urgent situation like the current health crisis. Finally, we explore 50 errors made by the best performing models on LitCovid documents and find that they often (1) correlate certain labels too closely together and (2) fail to focus on discriminative sections of the articles; both of which are important issues to address in future work. Both data and code are available on GitHub.
has issue date	2020-06-15(xsd:dateTime)
has license	arxiv
sha1sum (hex)	85bb1317013141d5e5f41d18376cd7257c46264a
resource representing a document's title	Document Classification for COVID-19 Literature
resource representing a document's body	covid:85bb1317013141d5e5f41d18376cd7257c46264a#body_text
is schema:about of	named entity 'models' named entity 'pandemic' named entity 'future' named entity 'MODEL' named entity 'BEST' »more»

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