Software Similar to MALLET

There are numerous other software packages relevant to machine learning and text that you might prefer over MALLET:

• NLTK (http://nltk.sourceforge.net) also has "Classifier" and "ClassifierTrainer" -style plug-and-play classifiers, as well as implementations of Naive Bayes, MaxEnt, feature selection, a "Token" class, finite state transducers with iterators over transitions. In addition it has parsing, facilities for tagging and information extraction.
• OpenNLP (http://opennlp.sourceforge.net) is also a Java package intended for text processing. It also has rich Pipelines consisting of chains of individual pipe Foo2Bar component steps that can be arbitrarily configured and plugged together.
• There is information about various Finite State Machine software at http://www.cs.jhu.edu/~jason/405/software.html, including pointers to AT&T Finite State package, which also has very general finite state transducers with iterators over transitions, arbitrary transition costs, generalized implementations of Viterbi and Forward Backward. In addition it has epsilon transitions, composition, and much more.
• Weka: Plug-and-play machine learning components in Java http://www.cs.waikato.ac.nz/~ml/weka, including classes for "Classifier", "NaiveBayes", "DecisionStump", "LogisticRegression", etc. It also has methods for splitting training sets, and nice evaluation tools, and GUI components to boot.
• Orange, component-based data mining software in C++ includes SVM, logistic regression, clustering, and lots more. http://magix.fri.uni-lj.si/orange/.
• Libbow http://www.cs.cmu.edu/~mccallum/bow also has mechanisms for feature extraction pipelines, plug-and-play classifiers, feature selection, clustering. It is written in C.
• COLT: High Performance Scientific Computing in Java: http://tilde-hoschek.home.cern.ch/~hoschek/colt.
• There are Java applets for various machine learning algorithms at http://www.cse.unsw.edu.au/~cs9417.
• Bayesian Networks in Java: http://www-2.cs.cmu.edu/~javabayes.
• Fe Sha and Fernando Pereira have written a CRF implementation in Java.
• Jonathan Baxter wrote much machine learning code in the late 1990's, including a rich "optimization" package.
• Ray Mooney has also been writing machine learning code in Java lately.