Projects

Natural Language Toolkit (NLTK)

NLTK — the Natural Language Toolkit — is a suite of open source Python modules, linguistic data and documentation for research and development in natural language processing, supporting dozens of NLP tasks, with distributions for Windows, Mac OSX and Linux.

Apache OpenNLP

claimed by Apache Software Foundation

Apache OpenNLP is a Java machine learning toolkit for natural language processing (NLP).

Treex - NLP Framework

Treex (formerly TectoMT) is a highly modular NLP software system implemented in Perl programming language under Linux. It is primarily aimed at Machine Translation, making use of the ideas and technology created during the Prague Dependency Treebank project. At the same time, it is also hoped to ... [More] significantly facilitate and accelerate development of software solutions of many other NLP tasks, especially due to re-usability of the numerous integrated processing modules (called blocks), which are equipped with uniform object-oriented interfaces. [Less]

fast-random-forest

What is FastRandomForest?FastRandomForest is a re-implementation of the Random Forest classifier (RF) for the Weka environment that brings speed and memory use improvements over the original Weka RF. Speed gains depend on many factors, but a 5-10x increase over Weka 3-6-1 on a quad core computer ... [More] is not uncommon, along with a 1.5x reduction in memory use. For detailed tests of speed and classification accuracy, as well as description of optimizations in the code, please refer to the FastRandomForest wiki at http://code.google.com/p/fast-random-forest/w or email the author at fran.supek\AT\irb.hr. Unrelated to the FastRF project, an MPI-enabled version of the Random Forest algorithm written in Fortran 90 is available from http://parf.googlecode.com. LicenseThis program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. Using from own Java codeJust add FastRandomForest.jar to your Java VM classpath by using the -cp switch, or by changing project dependencies in NetBeans/Eclipse/whatever IDE you use. Then use hr.irb.fastRandomForest.FastRandomForest as you would use any other classifier, see instructions at the WekaWiki: http://weka.sourceforge.net/wiki/index.php/Use_Weka_in_your_Java_code Using from Weka Explorer or Experimenter (versions 3.7.0, 3.6.1, 3.5.7 or earlier)1. Add the FastRandomForest.jar to your Java classpath when starting Weka. This is normally done by editing the line beginning with “cp=” in “RunWeka.ini” If "cp=" doesn't exist, search for "cmd_default=" and add after "#wekajar#;". 2. You need to extract the “GenericPropertiesCreator.props” file from your weka.jar (jar files are in fact ordinary zip archives, the GenericPropertiesCreator.props is under /weka/gui). 3. Place the file you've just extracted into the directory where you have installed Weka (on Windows this is commonly "C:\Program Files\Weka-3-6") 4. Under the # Lists the Classifiers-Packages I want to choose fromheading, add the line hr.irb.fastRandomForestDo not forget to add a comma and a backslash to the previous line. 5. Use the “FastRandomForest” class is in the hr.irb.fastRandomForest package in the "Classify" tab. The other three classes cannot be used directly. Using from Weka Explorer or Experimenter (versions 3.5.8 or 3.6.0 only)1. Add the FastRandomForest.jar to your Java classpath when starting Weka. This is normally done by editing the line beginning with “cp=” in “RunWeka.ini” 2. Extract the “GenericObjectEditor.props” file from weka.jar (jar files are in fact ordinary zip archives, the GenericObjectEditor.props is under /weka/gui). 3. Place the file you've just extracted into the directory where you have installed Weka (on Windows this is commonly "C:\Program Files\Weka-3-5") 4. Find the # Lists the Classifiers I want to choose fromheading and scroll far down to the end of the block (first empty line), then add a line: hr.irb.fastRandomForest.FastRandomForestDo not forget to append a comma and a backslash to the previous line. 5. The “FastRandomForest” class is in the "hr.irb.fastRandomForest" package in the "Classify" tab. Enjoy. [Less]

TestEl

TestEl is a Java-based learning analyzer for HTML (and possibly other) structured documents. It can be trained to detect structures in such documents and renders hits in XML.

CRM114 for Ruby

Ruby interface to the CRM114 Controllable Regex Mutilator, an advanced and fast text classifier that uses sparse binary polynomial matching with a Bayesian Chain Rule evaluator and a hidden Markov model to categorize data with up to a 99.87% accuracy.

SpamBayes for World of Warcraft

The coolest bayesian antispam addon for World of Warcraft that uses the original SpamBayes algorythm

Simple-Naive-Bayes-Classifier-for-PHP

Very basic implementation of Naive Bayes Classifier in PHP

Linux layer 7 packet classifier

l7-filter is an application layer packet classifier that can differentiate types of network traffic by protocol; for example it can identify bittorrent, IRC, SIP and many other types of traffic. In turn, these classifiers can be used to block or shape traffic according to a defined network policy.

Ruby LinkParser

A high-level interface to the CMU Link Grammar. This binding wraps the link-grammar shared library provided by the AbiWord project for their grammar-checker.