GROBID

Synopsis

"Grobid is a machine learning library for extracting, parsing and TEI-encoding of bibliographical information at large, with a particular focus on technical and scientific articles."<ref>https://github.com/kermitt2/grobid</ref> See a brief overview.

Features

According to the Grobid website:<ref>https://github.com/kermitt2/grobid</ref>

• Written in Java (with JNI call).
• High performance - on a modern but low profile MacBook Pro: header extraction from 4000 PDF in 10 minutes, parsing of 3000 references in 18 seconds.
• Modular and reusable machine learning models. The extractions are based on Linear Chain Conditional Random Fields which is currently the state of the art in bibliographical information extraction and labeling.
• Full encoding in TEI, both for the training corpus and the parsed results.
• Reinforcement of extracted bibliographical data via online call to Crossref (optional), export in OpenURL, etc. for easier integration into Digital Library environments.
• Rich bibliographical processing: fine grained parsing of author names, dates, affiliations, addresses, etc. but also quite reliable automatic attachment of affiliations to corresponding authors.
• "Automatic Generation" of pre-formatted training data based on new pdf documents, for supporting semi-automatic training data generation.

User commentary

Please sign all comments.

System requirements

"Grobid should run properly on MacOS X, Linux (32 & 64) and Windows (32) environments 'out of the box'."<ref>https://github.com/kermitt2/grobid</ref>

Source code and licensing

"Grobid is distributed under Apache 2.0 license."<ref>https://github.com/kermitt2/grobid</ref>

Support for TEI

Output created in TEI P5 XML.

Language(s)

"Written in Java (with JNI call)."<ref>https://github.com/kermitt2/grobid</ref>

Documentation

http://grobid.readthedocs.org/

How to download or buy

https://github.com/kermitt2/grobid

References

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