GROBID - Revision history

Kshawkin: /* Synopsis */ new first sentence of description from https://github.com/kermitt2/grobid

2017-02-16T16:47:56Z

‎Synopsis: new first sentence of description from https://github.com/kermitt2/grobid

Kshawkin: /* Sample implementations */ new online demo

2017-01-05T14:55:31Z

‎Sample implementations: new online demo

Kshawkin: moved Grobid to GROBID: It seems that's what they call it

2016-09-06T15:18:37Z

moved Grobid to GROBID: It seems that's what they call it

Kshawkin: /* Sample implementations */ added link to demo

2015-11-08T23:44:29Z

‎Sample implementations: added link to demo

Kshawkin: /* Documentation */ updated link

2015-11-08T23:43:19Z

‎Documentation: updated link

Kshawkin: /* Synopsis */ link to a brief overview

2015-11-08T23:42:43Z

‎Synopsis: link to a brief overview

Kshawkin: Created page with "Category:Tools Category:Conversion and preprocessing tools == Synopsis == "Grobid is a machine learning library for extracting, parsing and TEI-encoding of bibliographi..."

2014-11-25T17:27:00Z

Created page with "Category:Tools Category:Conversion and preprocessing tools == Synopsis == "Grobid is a machine learning library for extracting, parsing and TEI-encoding of bibliographi..."

New page

[[Category:Tools]]

[[Category:Conversion and preprocessing tools]]

== 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>

== 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 [http://www.apache.org/licenses/LICENSE-2.0 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 ==
See "Usage" section of https://github.com/kermitt2/grobid .

== Tech support ==

== User community ==

== Sample implementations ==

== Current version number and date of release ==

== History of versions ==

== How to download or buy ==
https://github.com/kermitt2/grobid

== Additional notes ==

== References ==
<references/>

@@ Line 4: / Line 4: @@
 == 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 [http://ercim-news.ercim.eu/en100/r-i/grobid-information-extraction-from-scientific-publications brief overview].
+"GROBID is a machine learning library for extracting, parsing and re-structuring raw documents such as PDF into structured TEI-encoded documents with a particular focus on technical and scientific publications."<ref>https://github.com/kermitt2/grobid</ref> See a [http://ercim-news.ercim.eu/en100/r-i/grobid-information-extraction-from-scientific-publications brief overview].
 == Features ==

@@ Line 42: / Line 42: @@
 == Sample implementations ==
-[http://scite-it.eu/ demo site]
+* [http://scite-it.eu/ demo site] (not working as of 2017-01-05)
 == Current version number and date of release ==

← Older revision	Revision as of 15:18, 6 September 2016
(No difference)

@@ Line 33: / Line 33: @@
 == Documentation ==
-See "Usage" section of https://github.com/kermitt2/grobid .
+http://grobid.readthedocs.org/
 == Tech support ==