EDLIN - The Machine learning framework of Ontotext

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.

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.

Edlin Basics

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

Feature Extraction module

A module for feature extraction.

Edlin-Wrapper(for GATE)

Edlin-Wrapper wraps the algorithms of Edlin, so that they can be used in GATE 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]

Mallet-Wrapper(for GATE)

Mallet-Wrapper wraps the algorithms of Mallet, so that they can be used in GATE for multiple information extraction purposes.
The algorithms are wrapped as ProcessingResources and LanguageResources and can be applied directly in a pipeline.

Document classification API(DAPI).

Currently not part of Edlin.