Metadata-Version: 2.0
Name: TUPA
Version: 1.3.4
Summary: Transition-based UCCA Parser
Home-page: https://github.com/huji-nlp/tupa
Author: Daniel Hershcovich
Author-email: danielh@cs.huji.ac.il
License: UNKNOWN
Description-Content-Type: UNKNOWN
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)
Classifier: Operating System :: POSIX :: Linux
Classifier: Programming Language :: Python :: 3.6
Classifier: Topic :: Text Processing :: Linguistic
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Dist: numpy (>=1.12.1)
Requires-Dist: cython (>=0.26.1)
Requires-Dist: tqdm (>=4.23.3)
Requires-Dist: configargparse (>=0.13.0)
Requires-Dist: ucca (<1.1,>=1.0.104)
Requires-Dist: semstr[amr] (==1.0.48)
Requires-Dist: dynet (==2.0.3)
Requires-Dist: logbook (==1.4.0)
Provides-Extra: server
Requires-Dist: Flask (>=0.12.2); extra == 'server'
Requires-Dist: Flask-Assets (>=0.12); extra == 'server'
Requires-Dist: Flask-Compress (>=1.4.0); extra == 'server'
Requires-Dist: Jinja2 (>=2.9.6); extra == 'server'
Requires-Dist: matplotlib (>=2.0.2); extra == 'server'
Requires-Dist: networkx (>=1.11); extra == 'server'
Requires-Dist: webassets (>=0.12.1); extra == 'server'
Provides-Extra: viz
Requires-Dist: scipy; extra == 'viz'
Requires-Dist: pillow; extra == 'viz'
Requires-Dist: matplotlib; extra == 'viz'

Transition-based UCCA Parser
============================

TUPA is a transition-based parser for `Universal Conceptual Cognitive
Annotation (UCCA) <http://github.com/huji-nlp/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 English
Wiki
corpus <https://github.com/UniversalConceptualCognitiveAnnotation/UCCA_English-Wiki>`__),
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 <https://github.com/huji-nlp/tupa/releases/download/v1.3.3/ucca-bilstm-1.3.3.tar.gz>`__,
run:

::

    curl -LO https://github.com/huji-nlp/tupa/releases/download/v1.3.3/ucca-bilstm-1.3.3.tar.gz
    tar xvzf ucca-bilstm-1.3.3.tar.gz

Run the parser using the model:

::

    python -m tupa example.txt -m models/ucca-bilstm

Other languages
~~~~~~~~~~~~~~~

To get `a
model <https://github.com/huji-nlp/tupa/releases/download/v1.3.3/ucca-bilstm-1.3.3-fr.tar.gz>`__
pre-trained on the `French *20K Leagues*
corpus <https://github.com/UniversalConceptualCognitiveAnnotation/UCCA_French-20K>`__
or `a
model <https://github.com/huji-nlp/tupa/releases/download/v1.3.3/ucca-bilstm-1.3.3-de.tar.gz>`__
pre-trained on the `German *20K Leagues*
corpus <https://github.com/UniversalConceptualCognitiveAnnotation/UCCA_German-20K>`__,
run:

::

    curl -LO https://github.com/huji-nlp/tupa/releases/download/v1.3.3/ucca-bilstm-1.3.3-fr.tar.gz
    tar xvzf ucca-bilstm-1.3.3-fr.tar.gz
    curl -LO https://github.com/huji-nlp/tupa/releases/download/v1.3.3/ucca-bilstm-1.3.3-de.tar.gz
    tar xvzf ucca-bilstm-1.3.3-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

Author
------

-  Daniel Hershcovich: danielh@cs.huji.ac.il

Citation
--------

If you make use of this software, please cite `the following
paper <http://aclweb.org/anthology/P17-1104>`__:

::

    @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 <https://github.com/huji-nlp/tupa/releases/tag/v1.0>`__. To
reproduce the experiments, run:

::

    curl -L https://raw.githubusercontent.com/huji-nlp/tupa/master/experiments/acl2017.sh | bash

If you use the French, German or multitask models, please cite `the
following paper <http://www.cs.huji.ac.il/~danielh/acl2018.pdf>`__:

::

    @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
`v1.3.3 <https://github.com/huji-nlp/tupa/releases/tag/v1.3.3>`__. To
reproduce the experiments, run:

::

    curl -L https://raw.githubusercontent.com/huji-nlp/tupa/master/experiments/acl2018.sh | bash

License
-------

This package is licensed under the GPLv3 or later license (see
```LICENSE.txt`` <LICENSE.txt>`__).

|Build Status (Travis CI)| |Build Status (AppVeyor)| |PyPI version|

.. |Build Status (Travis CI)| image:: https://travis-ci.org/danielhers/tupa.svg?branch=master
   :target: https://travis-ci.org/danielhers/tupa
.. |Build Status (AppVeyor)| image:: https://ci.appveyor.com/api/projects/status/github/danielhers/tupa?svg=true
   :target: https://ci.appveyor.com/project/danielh/tupa
.. |PyPI version| image:: https://badge.fury.io/py/TUPA.svg
   :target: https://badge.fury.io/py/TUPA


