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h2. 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:
* inputs are represented as sparse document-term matrices
* parallel computation is used whenever possible (in order to deal with very large datasets)
* specific evaluation metrics such as Precision/Recall/F are being reported
* appropriate feature selection methods are added in order to reduce dimensionality, etc.

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|https://svn.ontotext.com/svn/kim/others/edlin/trunk/].


h2. Edlin Basics

Edlin Basics is the core of the tool, containing all ML algorithms, divided into two general groups: classification and sequence (tagging).

h3. Algorithms for classification
h5. Maxent
h5. Perceptron
h5. Naive Bayes
h5. MIRA

h3. Algorithms for sequence
h5. CRF
h5. Perceptron

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.