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on Dec 05, 2014 16:53.

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h5. Perceptron

The structured perceptron, with parallelization is implemented after : McDonald et al. (2010), ([Distributed Training Strategies for the Structured Perceptron | http://aclweb.org/anthology/N/N10/N10-1069.pdf])


h3. Algorithms for feature selection
NLP datasets are characterized by a large number of features, sometimes order of magnitudes higher than the number of training samples available. In order to avoid overfitting, feature selection can be used prior to or during model training. We have a large number of approaches to feature selection:
* Filter by Fisher test (association between feature and outcome), either keeping a percent of features, or keeping the features yielding small enough p-value.
* Filter by mutual information (between feature and outcome), either keeping a percent of features, or keeping the features yielding large enough mutual information
* A feature induction algorithm, described here: ([Tolosi et. al. 2013 A Feature Induction Algorithm with Application to Named Entity Disambiguation. RANLP 2013|http://www.aclweb.org/anthology/R13-1089])


h2. Feature Extraction module

A module for feature extraction.
A module for feature extraction: classification instances are produced automatically, as features are extracted from documents using a set of [Groovy|http://groovy.codehaus.org/] rules.

h2. Edlin-Wrapper(for GATE)