Projects / The Quipu Maximum Entropy Package

The Quipu Maximum Entropy Package

Maximum entropy is a powerful method for constructing statistical models of classification tasks, such as part-of-speech tagging in Natural Language Processing. The Quipu Maximum Entropy Package is a Java implementation of the maximum entropy framework. It allows you to train, evaluate, and use maxent models.


Recent releases

  •  05 Apr 2003 01:10

    Release Notes: This is an important release in which a number of corrections were made to the GIS algorithm that improve the speed and accuracy of the training algorithm. Lots of bugfixes were also made.

    •  21 Nov 2001 15:05

      Release Notes: There is now a smoothing option for training models, but it is not well tested. It will either improve or degrade your model's performance, depending on how much data you have and how many classes the model must consider. There is also a sample available in the source download, named maxent/samples/sports. It should be very helpful for newbies, but it is also useful as a quick test app when working on the maxent package itself. A few bug fixes and convenience classes were added.

      •  12 Oct 2001 19:59

        Release Notes: LGPL licensing, a new BasicEnglishAffixes class to perform basic morphological stemming for English, a fix for a bug in which the model would not train properly in situations where the number of features was constant, changing the top level package name from quipu to opennlp, lots of little tweaks to reduce memory consumption, a new subpackage, model training no longer automatically persists the new model and relies on EventStreams rather than EventCollectors, a thread-safe GISModel, and other changes.

        •  08 Dec 2000 13:58

          Release Notes: The GIS algorithm and the GISModel were reworked to use efficient data structures, giving better memory and speed performance on classification tasks which have many outcomes (without damaging performance for binary decisions). More code documentation was added. This version is fully compatible with models built using v0.2.0.

          •  06 Jun 2000 12:57

            Release Notes: Initial release.


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