  Bibliographic reference parsers extract metadata (e.g. author names, title,
year) from bibliographic reference strings. No reference parser consistently
gives the best results in every scenario. For instance, one tool may be best in
extracting titles, and another tool in extracting author names. In this paper,
we address the problem of reference parsing from a recommender-systems
perspective. We propose ParsRec, a meta-learning approach that recommends the
potentially best parser(s) for a given reference string. 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 field of
a reference string. The second approach achieved a 2.6% increase in F1 (0.909
vs. 0.886, p < 0.001) 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).
