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 Largin Margin Structured Perceptron
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Author: Eraldo R. Fernandes
(C) 2011

Acknowledgement
---------------
This implementation has been and is conducted through the help of several
colaborators. Specially, I would like to thank Ulf Brefeld and Ruy L. Milidiú.
Both have given many relevant advices and ideas during the development of this
framework.

What is structured learning?
----------------------------
Structured learning consists in learning a mapping from inputs to structured
outputs by means of a sample of correct input-output pairs. Many important
problems fit in this setting. For instance, dependency parsing involves the
recognition of a tree underlying a sentence.

What is structured perceptron?
------------------------------
Structured perceptron is a training algorithm for structured problems that is a
generalization of the binary perceptron. It learns the parameters of a linear
discriminant function that, given an input, discriminates the correct output
structure from the alternative ones by means of a task-specific optimization
problem.

What is included in this framework?
-----------------------------------
There are implementations of some variations of the structured perceptron
algorithm. The are also three instantiations of the framework. Namely, for
sequence labeling, dependency parsing and quotation extraction. The package also
includes a dual implementation of the structured perceptron that can handle
kernel functions.

How to execute an included instance of the framework?
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The common entry point for all instantiations is the command

  java br.pucrio.inf.learn.structlearning.discriminative.driver.Driver

When executed, this command shows a list available sub-commands.
For instance, you have the sub-command TrainDP to train and evaluate dependency
parsing models. You can execute each sub-command without parameters to access
their list of options.