Metadata-Version: 1.1
Name: TUPA
Version: 1.3.2
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
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 3.6
        
        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 <https://github.com/huji-nlp/ucca-corpora/tree/master/wiki/xml>`__),
        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 <https://github.com/huji-nlp/ucca-corpora/tree/master/vmlslm/fr>`__
        or the `German *20K Leagues*
        corpus <https://github.com/huji-nlp/ucca-corpora/tree/master/20k_de>`__,
        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
        
        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 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 <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
        `v1.3.2 <https://github.com/huji-nlp/tupa/releases/tag/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>`__).
        
        |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
        
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
