  Bibliographic reference parsers extract machine-readable metadata such as
author names, title, journal, and year from bibliographic reference strings. To
extract the metadata, the parsers apply heuristics or machine learning.
However, no reference parser, and no algorithm, consistently gives the best
results in every scenario. For instance, one tool may be best in extracting
titles in ACM citation style, but only third best when APA is used. Another
tool may be best in extracting English author names, while another one is best
for noisy data (i.e. inconsistent citation styles). In this paper, which is an
extended version of our recent RecSys poster, we address the problem of
reference parsing from a recommender-systems and meta-learning perspective. We
propose ParsRec, a meta-learning based recommender-system that recommends the
potentially most effective parser for a given reference string. ParsRec
recommends one out of 10 open-source parsers: Anystyle-Parser, Biblio, CERMINE,
Citation, Citation-Parser, GROBID, ParsCit, PDFSSA4MET, Reference Tagger, and
Science Parse. We evaluate ParsRec on 105k references from chemistry. We
propose two approaches to meta-learning recommendations. The first approach
learns the best parser for an entire reference string. The second approach
learns the best parser for each metadata type in a reference string. The second
approach achieved a 2.6% increase in F1 (0.909 vs. 0.886) over the best single
parser (GROBID), reducing the false positive rate by 20.2% (0.075 vs. 0.094),
and the false negative rate by 18.9% (0.107 vs. 0.132).
