Treeler is an open-source c++ library of structure prediction methods, focusing on Natural Language Processing tasks like tagging and parsing. It is released under the GNU-GPL.

Treeler implements a framework for linear structure prediction. The main features of the framework are:

[1]Structured prediction models take the form of factored predictors. A central component behind a structured prediction model is a factorization or decomposition of structures into "parts". The model is then defined according to this factorization.
[2]The library provides learning algorithms that are generic with respect to the particular factorization employed by a model. So far, the library implements learning algorithms for classification that have been ported to structure prediction. This includes Perceptron, log-linear models, and max-margin methods.
[3]The library provides standard factored models for multiclass classification, sequence tagging and dependency parsing.




Technical Requirements:

A typical Linux box with usual development tools: bash, make, and a C++ compiler with basic STL support.
Enough hard disk space (about 120Mb)
Some external libraries are required to compile FreeLing:
- libpcre (version 4.3 or higher): Perl C Regular Expressions. Included in most usual Linux distributions. You'll need binary and development packages.
- libdb (version 4.1.25 or higher): Berkeley DB. Included in all usual Linux distributions.


Treeler is still at an early stage of development. The current release is not ready for serious usage: the API might still change significantly, and at this point there is not documentation for the high-end user.

If you're still curious, you can get a copy of treeler from our svn repository:

svn co




For an overview of Treeler, check the video presentation at WAPA'2011 .


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