DeSR (c) Giuseppe Attardi 2005-2012
DeSR is a statistical dependency parser for natural languages.
DeSR can be trained from annotated corpora like those supplied in the
CoNLL 2006 and 2007 shared tasks.
Issue configure from this directory:
then build the parser with:
This will produce the following program:
If compiling on a 64-bit machine, code that will run both on 32-bit and
64-bit machines can be generated by issuing:
> configure enable-32bit
before performing make.
The 64-bit version is required though if you need to handle models larger than 4GB.
Several classifiers are included in the package:
Multi Layer Perceptron (with help from Joseph Turian)
SVM (using libSVM code from http://www.csie.ntu.edu.tw/~cjlin/libsvm)
Averaged Perceptron (by Massimilano Ciaramita)
Passive Aggressive Perceptron
3. TRAINING and PARSING
Training the parser requires an annotated corpus in the CoNNL tab format.
The parsing model to use is supplied in a configuration file,
which defaults to:
The parser models used for the CoNLL 2007 shared task are available in the
A visual tool for creating annotated corpora is available at:
The home page for the project is:
The code is available on Sourceforge at: