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GROBID

GROBID documentation

Visit the GROBID documentation for more detailed information.

Purpose

GROBID (or Grobid, but not GroBid nor GroBiD) means GeneRation Of BIbliographic Data.

GROBID is a machine learning library for extracting, parsing and re-structuring raw documents such as PDF into structured XML/TEI encoded documents with a particular focus on technical and scientific publications. First developments started in 2008 as a hobby. In 2011 the tool has been made available in open source. Work on GROBID has been steady as a side project since the beginning and is expected to continue until at least 2020 :)

The following functionalities are available:

Header extraction and parsing from article in PDF format. The extraction here covers the usual bibliographical information (e.g. title, abstract, authors, affiliations, keywords, etc.).
References extraction and parsing from articles in PDF format, around .85 f-score against a PubMed Central evaluation set. All the usual publication metadata are covered.

Citation contexts recognition and linking to the full bibliographical references of the article. The accuracy of citation contexts resolution is around 0.75 f-score (which corresponds to both the correct identification of the citation callout and its correct association with a full bibliographical reference).

Parsing of references in isolation (with around 0.89 f-score).
Parsing of names (e.g. person title, fornames, middlename, etc.), in particular author names in header, and author names in references (two distinct models).
Parsing of affiliation and address blocks.
Parsing of dates, ISO normalized day, month, year.
Full text extraction and structuring from PDF articles, including a model for the the overall document segmentation and a model for the structuring of the text body (paragraph, section titles, reference callout, figure, table, etc.).
Consolidation/resolution of the extracted bibliographical references using the biblio-glutton service or the CrossRef REST API. In both cases, DOI resolution performance is higher than 0.95 f-score from PDF extraction.
Extraction and parsing of patent and non-patent references in patent publications.

In a complete PDF processing, GROBID manages 55 final labels used to build relatively fine-grained structures, from traditional publication metadata (title, author first/last/middlenames, affiliation types, detailed address, journal, volume, issue, pages, etc.) to full text structures (section title, paragraph, reference markers, head/foot notes, figure headers, etc.).

GROBID includes a comprehensive web service API, batch processing, a JAVA API, a Docker image, a generic evaluation framework (precision, recall, etc., n-fold cross-evaluation) and the semi-automatic generation of training data.

GROBID can be considered as production ready. Deployments in production includes ResearchGate, HAL Research Archive, the European Patent Office, INIST-CNRS, Mendeley, CERN (Invenio), and many more. The tool is designed for high scalability in order to address the full scientific literature corpus.

GROBID should run properly "out of the box" on Linux (64 bits), MacOS, and Windows (32 and 64 bits).

For more information on how the tool works, on its key features and performance, visit the GROBID documentation.

Demo

For testing purposes, a public GROBID demo server is available at the following address: http://grobid.science-miner.com

The Web services are documented here.

Warning: Some quota and query limitation apply to the demo server! Please be courteous and do not overload the demo server.

Clients

For helping to exploit GROBID service at scale, we provide clients written in Python, Java, node.js using the web services for parallel batch processing:

<a href="https://github.com/kermitt2/grobid-client-python" target="_blank">Python GROBID client</a>
<a href="https://github.com/kermitt2/grobid-client-java" target="_blank">Java GROBID client</a>
<a href="https://github.com/kermitt2/grobid-client-node" target="_blank">Node.js GROBID client</a>

All these clients will take advantage of the multi-threading for scaling large set of PDF processing. As a consequence, they will be much more efficient than the batch command lines (which use only one thread) and should be prefered.

We have been able recently to run the complete fulltext processing at around 10.6 PDF per second (around 915,000 PDF per day, around 20M pages per day) with the node.js client listed above during one week on a 16 CPU machine (16 threads, 32GB RAM, no SDD, articles from mainstream publishers), see here (11.3M PDF were processed in 6 days by 2 servers without crash).

In addition, a Java example project is available to illustrate how to use GROBID as a Java library: https://github.com/kermitt2/grobid-example. The example project is using GROBID Java API for extracting header metadata and citations from a PDF and output the results in BibTeX format.

GROBID Modules

A series of additional modules have been developed for performing structure aware text mining directly on scholar PDF, reusing GROBID's PDF processing and sequence labelling weaponery:

grobid-ner: named entity recognition
grobid-quantities: recognition and normalization of physical quantities/measurements
software-mention: recognition of software mentions and attributes in scientific literature
grobid-astro: recognition of astronomical entities in scientific papers
grobid-bio: a bio-entity tagger using BioNLP/NLPBA 2004 dataset
grobid-dictionaries: structuring dictionaries in raw PDF format
grobid-superconductors: recognition of superconductor material and properties in scientific literature