Labeled data for citation field extraction

Thai, Dung, University of Massachusetts Amherst

Xu, Zhiyang, University of Massachusetts Amherst

Monath, Nicholas, University of Massachusetts Amherst

Veytsman, Boris, Chan Zuckerberg Initiative (United States), https://orcid.org/0000-0003-4674-8113

McCallum, Andrew, University of Massachusetts Amherst

dthai@cs.umass.edu, zhiyangxu@cs.umass.ed, nmonath@cs.umass.edu, bveytsman@chanzuckerberg.com, mccallum@cs.umass.edu

Published Mar 21, 2022 on Dryad. https://doi.org/10.5061/dryad.j0zpc86gj

Cite this dataset

Thai, Dung et al. (2022). Labeled data for citation field extraction [Dataset]. Dryad. https://doi.org/10.5061/dryad.j0zpc86gj

Abstract

Citations are an important part of scientific papers, and the proper handling of them is indispensable for the science of science. Citation field extraction is the task of parsing citations: given a citation string, extract authors, title, venue, doi etc. Since the number of citations is counted by hundreds millions, efficient computer based methods for this task are very important.

The development of machine learning methods for citation field extraction requires ground truth: a large corpus of labeled citations. This dataset provides a very large (41M) corpus of labeled data obtained by the reverse process: we took structured citation lists and used BibTeX to generate labeled citation strings.

Methods

We extracted structure citation lists from a variety of sources (see the description in our paper referenced below) and retypeset them using a variety of BibTeX styles. The styles were modified to produce the labels corresponding to citation fields.

Usage notes

See README file for data description.