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Bibliographic reference parsers extract metadata (e.g. author names, title,year) from bibliographic reference strings. No reference parser consistentlygives the best results in every scenario. For instance, one tool may be best inextracting titles, and another tool in extracting author names. In this paper,we address the problem of reference parsing from a recommender-systemsperspective. We propose ParsRec, a meta-learning approach that recommends thepotentially best parser(s) for a given reference string. We evaluate ParsRec on105k references from chemistry. We propose two approaches to meta-learningrecommendations. The first approach learns the best parser for an entirereference string. The second approach learns the best parser for each field ofa reference string. The second approach achieved a 2.6% increase in F1 (0.909vs. 0.886, p < 0.001) over the best single parser (GROBID), reducing the falsepositive rate by 20.2% (0.075 vs. 0.094), and the false negative rate by 18.9%(0.107 vs. 0.132).

ParsRec: Meta-Learning Recommendations for Bibliographic Reference Parsing