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h1. Edlin
Edlin is Ontotext's machine learning framework, written in JAVA. It was originally started by Georgi Georgiev and Kuzman Ganchev and is now actively developed by the Text-Analysis team of Ontotext.

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h2. Introduction
Edlin consists of four sub-projects(Basics, Edlin-Wrapper, Mallet-Wrapper and Feature Extraction) and is closely bound to yet another in-house project, Doc-Classif API(a.k.a DAPI).
The source can be found [here|https://svn.ontotext.com/svn/kim/others/edlin/trunk/].


h2. Edlin Basics
The core part of this framework - it contains all implementations algorithms.
They are divided in two general groups - classification and sequence(tagging).

h2. Feature Extraction module
A module for feature extraction.

h2. Edlin-Wrapper(for GATE)
Edlin-Wrapper wraps the algorithms of Edlin, so that they can be used in [GATE|http://gate.ac.uk/] for multiple information extraction purposes.
The algorithms are wrapped as ProcessingResources and LanguageResources and can be applied directly in a pipeline.
More about [Edlin-Wrapper|Edlin-Wrapper]

h2. Mallet-Wrapper(for GATE)
Mallet-Wrapper wraps the algorithms of [Mallet|http://mallet.cs.umass.edu/], so that they can be used in [GATE|http://gate.ac.uk/] for multiple information extraction purposes.
The algorithms are wrapped as ProcessingResources and LanguageResources and can be applied directly in a pipeline.

h2. Document classification API(DAPI).
Currently not part of Edlin.
Edlin ([Kuzman Ganchev and Georgi Georgiev, Edlin: an easy to read linear learning framework, RANLP, 2009.|http://www.aclweb.org/anthology/R09-1018]) is Ontotext's machine learning framework, written in JAVA, and designed to be easy to read and understand. It was started by Georgi Georgiev and Kuzman Ganchev and is now actively developed by the Text-Analysis team of Ontotext and become a fully featured, big data, enterprise grade machine learning framework.