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h1. Edlin
Edlin is Ontotext's machine learning framework, written in JAVA. It is intended to be light-weight and easy to use.
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.