Illinois Structured Learning Package (Illinois-SL) is a general purpose JAVA library for performing structured learning. It houses learning algorithms like averaged Structured Perceptron and Structured SVM with L2-Loss, and provides a minimal interface for your structured learning needs. The training algorithm employed for training SSVM is dual coordinate descent(DCD), which has been proven to have very good convergence properties. Illinois-SL comes with an efficient implementation of DCD with support for multi-threading. Illinois-SL provides a simple and neat framework for developing applications using structured prediction models.
To use Illinois-SL in your project add the following to your pom,
<dependencies>
...
<dependency>
<groupId>edu.illinois.cs.cogcomp</groupId>
<artifactId>illinois-sl-core</artifactId>
<version>1.0.0</version>
</dependency>
...
</dependencies>
<repositories>
...
<repository>
<id>CogcompSoftware</id>
<name>CogcompSoftware</name>
<url>http://cogcomp.cs.illinois.edu/m2repo/</url>
</repository>
...
</repositories>
We provide detailed examples in an accompanying package at illinois-sl-examples.
The Illinois Structured Learning Package is available under a Research
and Academic use license. For more details, view the license file LICENSE.
The Illinois Structured Learning Package was developed on and for GNU/Linux, specifically CENTOS (2.6.18-238.12.1.el5) and Scientific Linux (2.6.32-279.5.2.el6.x86_64). There are no guarantees for running it under any other operating system, but we hope it should run on a Linux OS without any issues.
We assume that the package is installed on a machine with sufficient memory. The actual requirement of the memory depends on the task and size of the learning problem.
NOTE: When running your project, if working with a large dataset, you may need to invoke your project using the -Xmx1G and -XX:MaxPermSize=1G JVM command line parameters.
Additional documentation is available in the JavaDoc located in doc/index.html
Please cite the following papers when using this library
M.-W. Chang, V. Srikumar, D. Goldwasser and Dan Roth. Structured output learning with indirect supervision. ICML, 2010.
K.-W. Chang, V. Srikumar, D. Roth. Multi-core Structural SVM Training. ECML, 2013.
Please open a new issue with a minimal working example, in case you run into problems when using this package, and we will assist you. You can also email your questions to illinois-ml-nlp-users@cs.uiuc.edu.
(C) 2015 Kai-Wei Chang, Shyam Upadhyay, Ming-Wei Chang, Vivek Srikumar and Dan Roth, Cognitive Computation Group, University of Illinois at Urbana-Champaign.