EDLIN - ML Framework

Edlin

Edlin (Kuzman Ganchev and Georgi Georgiev, Edlin: an easy to read linear learning framework, RANLP, 2009.) 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.

Introduction

Edlin is a collection of machine learning algorithms comprising a large number of state-of-the-art methods for classification and sequence tagging. Even though at their core they are general machine-learning approaches (perceptrons, logistic regression), the implementation is optimized for NLP learning tasks:

Edlin consists of four sub-projects(Basics, Edlin-Wrapper, Mallet-Wrapper and Feature Extraction). Below find technical details on each of the sub-projects. 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.