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.

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

Maximum Entropy (Maxent) is essentially multi-class logistic regression. It was first adapted and applied to NLP tasks by Berger, et al (1996) and Della Pietra, et al. 1997 (Adam L. Berger , Stephen A. Della Pietra , Vincent J. Della Pietra, A Maximum Entropy approach to Natural Language Processing, Journal of Computational Linguistics, 1996, vol. 22, pp. 39-71). Maxent is a linear model, where the posterior class probabilities are modelled as a linear combination of the input features. In order to fit the model weights to the training observations, a loglikelihood loss function is maximized. The maximization is carried out by some gradient ascent method.

Several parameters for maxent can be selected, such that the most appropriate and efficient model is trained to suite a particular dataset:

- Gradient method:
- Gradient ascent
- Conjugate gradient
- Stochastic gradient ascent (fast)

- Parallelization:
- Modified objective for targeted optimization of particular Precision/Recall trade-off:
- We implemented a weighted likelihood objective that allows for optimizing a specific F_beta, for a given beta, which means that we can specify a desired Precision/ Recall trade-off. In practice, we can therefore train models that have very high Precision, or very high Recall, at the expense of the complementary measure.

- Regularization:
- L1 regularization is often used in practice for sparse models and reducing overfitting. An L1-regularized maxent can also serve as feature selection procedure.

A module for feature extraction.

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 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.

Currently not part of Edlin.