* MALLET includes sophisticated tools for document classification: efficient routines for converting text to "features", a wide variety of algorithms (including Naïve Bayes, Maximum Entropy, and Decision Trees), and code for evaluating classifier performance using several commonly used metrics. * In addition to classification, MALLET includes tools for sequence tagging for applications such as named-entity extraction from text. Algorithms include Hidden Markov Models, Maximum Entropy Markov Models, and Conditional Random Fields. These methods are implemented in an extensible system for finite state transducers.

* The library includes greatly simplified syntax and allows the creation of interesting sketches with very few lines of code. * For programmers who want to push the envelope further, all functionality of the Java version of the library is maintained, so it is still possible to create intricate and more complex applications.