TUPA is a transition-based parser for Universal Conceptual Cognitive Annotation (UCCA).
- Python 3.x
- All dependencies for DyNet
Create a Python virtual environment:
virtualenv --python=/usr/bin/python3 venv
. venv/bin/activate # on bash
source venv/bin/activate.csh # on csh
Install the latest release:
pip install tupa
Alternatively, install the latest code from GitHub (may be unstable):
git clone https://github.com/danielhers/tupa
cd tupa
python setup.py install
The parser requires the English model for spaCy to be available. To install it run:
python -m spacy download en
Having a directory with UCCA passage files (for example, the Wiki corpus), run:
python -m tupa.parse -t <train_dir> -d <dev_dir> -c <model_type> -m <model_filename>
The possible model types are sparse, mlp and bilstm.
Run the parser on a text file (here named example.txt) using a trained model:
python -m tupa.parse example.txt -c <model_type> -m <model_filename>
An xml file will be created per passage (separate by blank lines in the text file).
To download and extract the pre-trained models, run:
curl --remote-name-all http://www.cs.huji.ac.il/~danielh/ucca/{sparse,mlp,bilstm}.tar.gz
tar xvzf sparse.tar.gz
tar xvzf mlp.tar.gz
tar xvzf bilstm.tar.gz
Run the parser using any of them:
python -m tupa.parse example.txt -c sparse -m models/sparse
python -m tupa.parse example.txt -c mlp -m models/mlp
python -m tupa.parse example.txt -c bilstm -m models/bilstm
- Daniel Hershcovich: [email protected]
If you make use of this software, please cite the following paper:
@InProceedings{hershcovich2017a,
author = {Hershcovich, Daniel and Abend, Omri and Rappoport, Ari},
title = {A Transition-Based Directed Acyclic Graph Parser for UCCA},
booktitle = {Proc. of ACL},
year = {2017},
pages = {1127--1138},
url = {http://aclweb.org/anthology/P17-1104}
}
The version of the parser used in the paper is v1.0. To reproduce the experiments from the paper, run in an empty directory (with a new virtualenv):
pip install "tupa>=1.0,<1.1"
mkdir pickle models
curl -L http://www.cs.huji.ac.il/~danielh/ucca/ucca_corpus_pickle.tgz | tar xz -C pickle
curl --remote-name-all http://www.cs.huji.ac.il/~danielh/ucca/{sparse,mlp,bilstm}.tgz
tar xvzf sparse.tgz
tar xvzf mlp.tgz
tar xvzf bilstm.tgz
python -m spacy download en
python -m scripts.split_corpus pickle -t 4282 -d 454 -l
python -m tupa.parse -c sparse -m models/ucca-sparse -Web pickle/test
python -m tupa.parse -c mlp -m models/ucca-mlp -Web pickle/test
python -m tupa.parse -c bilstm -m models/ucca-bilstm -Web pickle/test
This package is licensed under the GPLv3 or later license (see LICENSE.txt).
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