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This toolkit consists of implementations of various graph-based semi-supervised learning (SSL) algorithms. Currently, three algorithms are implemented: Gaussian Random Fields (GRF), Adsorption, and Modified Adsorption (MAD). Junto also contains Hadoop-based implementations of these three algorithms.
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parthatalukdar/junto
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---------------------------------------------------------- The Junto Label Propagation Toolkit Author: Partha Pratim Talukdar (partha@talukdar.net) Contributors: Jason Baldridge (jbaldrid@mail.utexas.edu) ---------------------------------------------------------- Introduction ============ This package provides an implementation of the Adsorption and Modified Adsorption (MAD) algorithms described in the following papers. Weakly Supervised Acquisition of Labeled Class Instances using Graph Random Walks. Talukdar et al., EMNLP 2008 New Regularized Algorithms for Transductive Learning. Partha Pratim Talukdar, Koby Crammer, ECML-PKDD 2009 Experiments in Graph-based Semi-Supervised Learning Methods for Class-Instance Acquisition. Partha Pratim Talukdar, Fernando Pereira, ACL 2010 Please cite Talukdar and Crammer (2009) and/or Talukdar and Pereira (2010) if you use this library. Additionally, LP_ZGL, one of the first label propagation algorithms is also implemented. Xiaojin Zhu and Zoubin Ghahramani. Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University, 2002. This file contains the configuration and build instructions. Why is the toolkit named Junto? The core code was written while Partha Talukdar was at the University of Pennsylvania, and Ben Franklin (the founder of the University) established a club called Junto that provided a structured forum for him and his friends to debate and exchange knowledge: http://en.wikipedia.org/wiki/Junto_(club) This has a nice parallel with how label propagation works: nodes are connected and influence each other based on their connections. Also "junto" means "along" and "together" in a number of Latin languages, and carries the connotation of cooperation---also a good fit for label propagation. Requirements ============ * Version 1.6 of the Java 2 SDK (http://java.sun.com) Configuring your environment variables ====================================== The easiest thing to do is to set the environment variables JAVA_HOME and JUNTO_DIR to the relevant locations on your system. Set JAVA_HOME to match the top level directory containing the Java installation you want to use. For example, on Windows: C:\> set JAVA_HOME=C:\Program Files\jdk1.5.0_04 or on Unix: % setenv JAVA_HOME /usr/local/java (csh) > export JAVA_HOME=/usr/java (ksh, bash) On Windows, to get these settings to persist, it's actually easiest to set your environment variables through the System Properties from the Control Panel. For example, under WinXP, go to Control Panel, click on System Properties, choose the Advanced tab, click on Environment Variables, and add your settings in the User variables area. Next, likewise set JUNTO_DIR to be the top level directory where you unzipped the download. In Unix, type 'pwd' in the directory where this file is and use the path given to you by the shell as JUNTO_DIR. You can set this in the same manner as for JAVA_HOME above. Next, add the directory JUNTO_DIR/bin to your path. For example, you can set the path in your .bashrc file as follows: export PATH="$PATH:$JUNTO_DIR/bin" On Windows, you should also add the python main directory to your path. Once you have taken care of these three things, you should be able to build and use the Junto Library. Note: Spaces are allowed in JAVA_HOME but not in JUNTO_DIR. To set an environment variable with spaces in it, you need to put quotes around the value when on Unix, but you must *NOT* do this when under Windows. Building the system from source =============================== Junto uses SBT (Simple Build Tool) with a standard directory structure. To build Junto, go to JUNTO_DIR and type: $ bin/build update compile This will compile the source files and put them in ./target/classes. If this is your first time running it, you will see messages about Scala being dowloaded -- this is fine and expected. Once that is over, the Junto code will be compiled. To try out other build targets, do: $ bin/build This will drop you into the SBT interface. The build targets that are supported are listeded here: https://github.com/harrah/xsbt/wiki/Getting-Started-Running Note: if you have SBT already installed on your system, you can also just call it directly with "sbt". If you wish to use Junto as an API, you can create a self-contained assembly jar by using the "assembly" action in SBT. Also, you can just do: $ bin/build assembly Trying it out ============= If you've managed to configure and build the system, you should be able to go to $JUNTO_DIR/examples/simple and run: $ junto config simple_config Please look into the examples/simple/simple_config file for various options available. Sample (dummy) data is made available in the examples/simple/data directory. A more extensive example on prepositional phrase attachment is in examples/ppa. See the README in that directory for more details. Hadoop ====== If you are interested in trying out the Hadoop implementations, then please look into examples/hadoop/README Bug Reports =========== Please report bugs on the GitHub site: https://github.com/parthatalukdar/junto Getting help ============ Documentation is admittedly thin. If you get stuck, you can get help by posting questions to the junto-open group: http://groups.google.com/group/junto-open
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This toolkit consists of implementations of various graph-based semi-supervised learning (SSL) algorithms. Currently, three algorithms are implemented: Gaussian Random Fields (GRF), Adsorption, and Modified Adsorption (MAD). Junto also contains Hadoop-based implementations of these three algorithms.
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