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Bibliographic reference parsers extract machine-readable metadata such asauthor names, title, journal, and year from bibliographic reference strings. Toextract the metadata, the parsers apply heuristics or machine learning.However, no reference parser, and no algorithm, consistently gives the bestresults in every scenario. For instance, one tool may be best in extractingtitles in ACM citation style, but only third best when APA is used. Anothertool may be best in extracting English author names, while another one is bestfor noisy data (i.e. inconsistent citation styles). In this paper, which is anextended version of our recent RecSys poster, we address the problem ofreference parsing from a recommender-systems and meta-learning perspective. Wepropose ParsRec, a meta-learning based recommender-system that recommends thepotentially most effective parser for a given reference string. ParsRecrecommends one out of 10 open-source parsers: Anystyle-Parser, Biblio, CERMINE,Citation, Citation-Parser, GROBID, ParsCit, PDFSSA4MET, Reference Tagger, andScience Parse. We evaluate ParsRec on 105k references from chemistry. Wepropose two approaches to meta-learning recommendations. The first approachlearns the best parser for an entire reference string. The second approachlearns the best parser for each metadata type in a reference string. The secondapproach achieved a 2.6% increase in F1 (0.909 vs. 0.886) over the best singleparser (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).

ParsRec: A Novel Meta-Learning Approach to Recommending Bibliographic Reference Parsers