Metadata-Version: 1.1
Name: TUPA
Version: 1.1.1
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: 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.x
        -  All `dependencies for
           DyNet <http://dynet.readthedocs.io/en/latest/python.html>`__
        
        Install
        ~~~~~~~
        
        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
        
        Train the parser
        ~~~~~~~~~~~~~~~~
        
        Having a directory with UCCA passage files (for example, `the Wiki
        corpus <https://github.com/huji-nlp/ucca-corpus/tree/master/wiki/pickle>`__),
        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``.
        
        Parse a text file
        ~~~~~~~~~~~~~~~~~
        
        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).
        
        Pre-trained models
        ~~~~~~~~~~~~~~~~~~
        
        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
        
        Author
        ------
        
        -  Daniel Hershcovich: danielh@cs.huji.ac.il
        
        Citation
        --------
        
        If you make use of this software, please cite `the following
        paper <http://www.cs.huji.ac.il/~danielh/acl2017.pdf>`__:
        
        ::
        
            @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 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
        
        License
        -------
        
        This package is licensed under the GPLv3 or later license (see
        ```LICENSE.txt`` <LICENSE.txt>`__).
        
        |Build Status| |PyPI version|
        
        .. |Build Status| image:: https://travis-ci.org/danielhers/tupa.svg?branch=master
           :target: https://travis-ci.org/danielhers/tupa
        .. |PyPI version| image:: https://badge.fury.io/py/TUPA.svg
           :target: https://badge.fury.io/py/TUPA
        
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
