Abstract
Accurate parsing of citation reference strings is crucial to automatically
construct scholarly databases such as Google Scholar or Semantic Scholar.
Citation field extraction (CFE) is precisely this task---given a reference
label which tokens refer to the authors, venue, title, editor, journal, pages,
etc. Most methods for CFE are supervised and rely on training from labeled
datasets that are quite small compared to the great variety of reference
formats. BibTeX, the widely used reference management tool, provides a natural
method to automatically generate and label training data for CFE. In this
paper, we describe a technique for using BibTeX to generate, automatically, a
large-scale 41M labeled strings), labeled dataset, that is four orders of
magnitude larger than the current largest CFE dataset, namely the UMass
Citation Field Extraction dataset Anzaroot and McCallum, 2013. We
experimentally demonstrate how our dataset can be used to improve the
performance of the UMass CFE using a RoBERTa-based Liu et al., 2019 model. In
comparison to previous SoTA, we achieve a 24.48% relative error reduction,
achieving span level F1-scores of 96.3%.
Description
[2006.05563] Using BibTeX to Automatically Generate Labeled Data for Citation Field Extraction
Links and resources
Tags
community