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Think about adding GROBIT for "PDF to BibTeX" functionality #327
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Sadly, it is not easy to integrate GROBID directly with JabRef, see kermitt2/grobid#250. We could run GROBID on a webserver, upload PDFs, analyze them on the server, post result back to JabRef. |
koppor
changed the title
Think about adding GROBIT
Think about adding GROBIT for "PDF to BibTeX" functionality
Aug 13, 2019
Alternative: http://excite.west.uni-koblenz.de/excite |
GROBIT is in place for references from plain text: #327 |
Fixed by JabRef#7947 |
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See https://arxiv.org/abs/1802.01168
Bibliographic reference parsing refers to extracting machine-readable metadata, such as the names of the authors, the title, or journal name, from bibliographic reference strings. Many approaches to this problem have been proposed so far, including regular expressions, knowledge bases and supervised machine learning. Many open source reference parsers based on various algorithms are also available. In this paper, we apply, evaluate and compare ten reference parsing tools in a specific business use case. The tools are Anystyle-Parser, Biblio, CERMINE, Citation, Citation-Parser, GROBID, ParsCit, PDFSSA4MET, Reference Tagger and Science Parse, and we compare them in both their out-of-the-box versions and tuned to the project-specific data. According to our evaluation, the best performing out-of-the-box tool is GROBID (F1 0.89), followed by CERMINE (F1 0.83) and ParsCit (F1 0.75). We also found that even though machine learning-based tools and tools based on rules or regular expressions achieve on average similar precision (0.77 for ML-based tools vs. 0.76 for non-ML-based tools), applying machine learning-based tools results in the recall three times higher than in the case of non-ML-based tools (0.66 vs. 0.22). Our study also confirms that tuning the models to the task-specific data results in the increase in the quality. The retrained versions of reference parsers are in all cases better than their out-of-the-box counterparts; for GROBID F1 increased by 3% (0.92 vs. 0.89), for CERMINE by 11% (0.92 vs. 0.83), and for ParsCit by 16% (0.87 vs. 0.75).
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