Welcome to Treeler
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:
- 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.
- 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.
- The library provides standard factored models for multiclass classification, sequence tagging and dependency parsing and semantic role labeling.
For an overview of Treeler, check the video presentation at WAPA'2011, or check the slides attached at the bottom of this page.
Treeler was developed at the TALP Research Centre of the UPC. The main authors are Xavier Carreras, Xavier Lluís, and Lluís Padró, with contributions from Andreu Mayo, Pranava Swaroop Madhyastha, and Manuel Bertran (from CLiC - Centre de Llenguatge i Computacio at University of Barcelona within the actions of the Araknion project).
Treeler originates from the EGSTRA library, which was originally written by Xavier Carreras and Terry Koo at MIT/CSAIL during 2006-2009. We thank Michael Collins and Amir Globerson for their contribution and support to the development of EGSTRA.
To get treeler, download it directly from our svn repository:
svn co http://devel.cpl.upc.edu/treeler/svn/trunk
The FreeLing project now includes statistical dependency parsers and semantic role labeling methods for English, Spanish and Catalan, using Treeler components. To simply use such methods, we strongly recommend to use FreeLing.
Treeler obtained financial support from the Pascal2 Harvest Programme, the XLike EU project, the KNOW2 Spanish project (TIN2009-14715-C04-01) and the Araknion Spanish project (FFI-2010-11474-E). We are grateful for their support.