Transition-based UCCA Parser
Project description
TUPA is a transition-based parser for Universal Conceptual Cognitive Annotation (UCCA).
Requirements
Python 3.x
Build
Install the required modules:
git submodule update --init --recursive virtualenv --python=/usr/bin/python3 . . bin/activate # on bash source bin/activate.csh # on csh pip install -r requirements.txt python -m spacy download en python ucca/setup.py install python setup.py install
Train the parser
Having a directory with UCCA passage files (for example, the Wiki corpus), run:
python tupa/parse.py -t <train_dir> -d <dev_dir> -m <model_filename>
To specify a model type (sparse, mlp or bilstm), add -c <model_type>.
Parse a text file
Run the parser on a text file (here named example.txt) using a trained model:
python tupa/parse.py example.txt -m <model_filename>
A file named example.xml will be created.
If you specified a model type using -c when training the model, be sure to include it when parsing too.
Pre-trained models
To download and extract the pre-trained models, run:
wget http://www.cs.huji.ac.il/~danielh/ucca/{sparse,mlp,bilstm}.tgz
tar xvzf sparse.tgz
tar xvzf mlp.tgz
tar xvzf bilstm.tgz
Run the parser using any of them:
python tupa/parse.py example.txt -c sparse -m models/ucca-sparse python tupa/parse.py example.txt -c mlp -m models/ucca-mlp python tupa/parse.py example.txt -c bilstm -m models/ucca-bilstm
Citation
If you make use of this software, please cite the following paper:
@inproceedings{hershcovich2017a,
title={A Transition-Based Directed Acyclic Graph Parser for {UCCA}},
author={Hershcovich, Daniel and Abend, Omri and Rappoport, Ari},
booktitle={Proc. of ACL},
year={2017}
}
License
This package is licensed under the GPLv3 or later license (see `LICENSE.txt <LICENSE.txt>`__).
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