swalter62/grobid

A machine learning software for extracting information from scholarly documents

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:

• Python GROBID client

• Java GROBID client

• Node.js GROBID client

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

Latest version

The latest stable release of GROBID is version 0.5.6. This version brings:

• Better abstract structuring (with citation contexts)
• n-fold cross evaluation and better evaluation report (thanks to @lfoppiano)
• Improved PMC ID and PMID recognition
• Improved subscript/superscript and font style recognition (via pdfalto)
• Improved JEP integration (support of python virtual environment for using DeLFT Deep Learning library, thanks @de-code and @lfoppiano)
• Several bug fixes (thanks @de-code, @bnewbold, @Vitaliy-1 and @lfoppiano)
• Improved dehyphenization (thanks to @lfoppiano)

(more information in the release page)

New in previous release 0.5.5:

• Using pdfalto instead of pdf2xml for the first PDF parsing stage, with many improvements in robustness, ICU support, unknown glyph/font normalization (thanks in particular to @aazhar)
• Improvement and full review of the integration of consolidation services, supporting biblio-glutton (additional identifiers and Open Access links) and Crossref REST API (add specific user agent, email and token for Crossref Metadata Plus)
• Fix bounding box issues for some PDF #330
• Updated lexicon #396

(more information in the release page)

New in previous release 0.5.4:

• Transparent usage of DeLFT deep learning models (BidLSTM-CRF/ELMo) instead of Wapiti CRF models, native integration via JEP
• Support of biblio-glutton as DOI/metadata matching service, alternative to crossref REST API
• Improvement of citation context identification and matching (+9% recall with similar precision, for PMC sample 1943 articles, from 43.35 correct citation contexts per article to 49.98 correct citation contexts per article)
• Citation callout now in abstract, figure and table captions
• Structured abstract (including update of TEI schema)
• Bug fixes and some more parameters: by default using all available threads when training (thanks @de-code) and possibility to load models at the start of the service

(more information in the release page)

New in previous release 0.5.3:

• Improvement of consolidation options and processing (better handling of CrossRef API, but the best is coming soon ;)
• Better recall for figure and table identification (thanks to @detonator413)
• Support of proxy for calling crossref with Apache HttpClient

(more information in the release page)

New in previous release 0.5.2:

• Corrected back status codes from the REST API when no available engine (503 is back again to inform the client to wait, it was removed by error in version 0.5.0 and 0.5.1 for PDF processing services only, see documentation of the REST API)
• Added Grobid clients for Java, Python and NodeJS
• Added metrics in the REST entrypoint (accessible via http://localhost:8071)
• Bugfixing

(more information in the release page)

New in previous release 0.5.1:

• Migrate from maven to gradle for faster, more flexible and more stable build, release, etc.
• Usage of Dropwizard for web services
• Move the Grobid service manual to readthedocs
• (thanks to @detonator413 and @lfoppiano for this release! future work in versions 0.5.* will focus again on improving PDF parsing and structuring accuracy)

(more information in the release page)

New in previous release 0.4.4:

• New models: f-score improvement on the PubMed Central sample, bibliographical references +2.5%, header +7%
• New training data and features for bibliographical references, in particular for covering HEP domain (INSPIRE), arXiv identifier, DOI and url (thanks @iorala and @michamos !)
• Support for CrossRef REST API (instead of the slow OpenURL-style API which requires a CrossRef account), in particular for multithreading usage (thanks @Vi-dot)
• Improve training data generation and documentation (thanks @jfix)
• Unicode normalisation and more robust body extraction (thanks @aoboturov)
• fixes, tests, documentation and update of the pdf2xml fork for Windows (thanks @lfoppiano)

(more information in the release page)

New in previous release 0.4.2:

• f-score improvement for the PubMed Central sample: fulltext +10-14%, header +0.5%, citations +0.5%
• More robust PDF parsing
• Identification of equations (with PDF coordinates)
• End-to-end evaluation with Pub2TEI conversions
• many fixes and refactoring

New in previous release 0.4.1:

• Support for Windows thanks to the contributions of Christopher Boumenot!
• Support to Docker.
• Fixes and refactoring.
• New web services for PDF annotation and updated web console application.
• Some improvements on figure/table extraction - but still experimental at this stage (work in progress, as the whole full text model).

New in previous release 0.4.0:

• Improvement of the recognition of citations thanks to refinements of CRF features - +4% in f-score for the PubMed Central sample.
• Improvement of the full text model, with new features and the introduction of two additional models for figures and tables.
• More robust synchronization of CRF sequence with PDF areas, resulting in improved bounding box calculations for locating annotations in the PDF documents.
• Improved general robustness thanks to better token alignments.

License

GROBID is distributed under Apache 2.0 license.

Main author and contact: Patrice Lopez ([email protected])

Sponsors

ej-technologies provided us a free open-source license for its Java Profiler. Click the JProfiler logo below to learn more.

Reference

For citing this work, you can refer to the present GitHub project, together with the Software Heritage project-level permanent identifier. For example, with BibTeX:

@misc{GROBID, 
    title = {GROBID}, 
    howpublished = {\url{https://github.com/kermitt2/grobid}}, 
    publisher = {GitHub},
    year = {2008 --- 2019},
    archivePrefix = {swh},
    eprint = {1:dir:6a298c1b2008913d62e01e5bc967510500f80710}
}

See the GROBID documentation for more related resources.