Transition-based UCCA Parser
Project description
TUPA is a transition-based parser for Universal Conceptual Cognitive Annotation (UCCA).
Requirements
Python 3.6
Install
Create a Python virtual environment. For example, on Linux:
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
Train the parser
Having a directory with UCCA passage files (for example, the Wiki corpus), run:
python -m tupa -t <train_dir> -d <dev_dir> -c <model_type> -m <model_filename>
The possible model types are sparse, mlp, and bilstm.
Parse a text file
Run the parser on a text file (here named example.txt) using a trained model:
python -m tupa example.txt -m <model_filename>
An xml file will be created per passage (separate by blank lines in the text file).
Pre-trained models
To download and extract a model pre-trained on the Wiki corpus, run:
curl -O https://github.com/huji-nlp/tupa/releases/download/v1.3.2/ucca-bilstm-1.3.2.tar.gz tar xvzf ucca-bilstm-1.3.2.tar.gz
Run the parser using the model:
python -m tupa example.txt -m models/ucca-bilstm
Other languages
To get a model pre-trained on the French *20K Leagues* corpus or the German *20K Leagues* corpus, run:
curl -O https://github.com/huji-nlp/tupa/releases/download/v1.3.2/ucca-bilstm-1.3.2-fr.tar.gz tar xvzf ucca-bilstm-1.3.2-fr.tar.gz curl -O https://github.com/huji-nlp/tupa/releases/download/v1.3.2/ucca-bilstm-1.3.2-de.tar.gz tar xvzf ucca-bilstm-1.3.2-de.tar.gz
Run the parser on a French/German text file (separate passages by blank lines):
python -m tupa exemple.txt -m models/ucca-bilstm-fr --lang fr python -m tupa beispiel.txt -m models/ucca-bilstm-de --lang de
Citation
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, run:
curl https://github.com/huji-nlp/tupa/blob/master/experiments/acl2017.sh | bash
If you use the French, German or multitask models, please cite the following paper:
@InProceedings{hershcovich2018multitask, author = {Hershcovich, Daniel and Abend, Omri and Rappoport, Ari}, title = {Multitask Parsing Across Semantic Representations}, booktitle = {Proc. of ACL}, year = {2018}, url = {http://www.cs.huji.ac.il/~danielh/acl2018.pdf} }
The version of the parser used in the paper is v1.3.2. To reproduce the experiments, run:
curl https://github.com/huji-nlp/tupa/blob/master/experiments/acl2018.sh | bash
License
This package is licensed under the GPLv3 or later license (see `LICENSE.txt <LICENSE.txt>`__).
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