Metadata-Version: 1.0
Name: TUPA
Version: 1.0.post4
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 |Build Status|
        ===========================================
        
        TUPA is a transition-based parser for `Universal Conceptual Cognitive
        Annotation (UCCA) <http://github.com/huji-nlp/ucca>`__.
        
        Requirements
        ~~~~~~~~~~~~
        
        -  Python 3.x
        
        Build
        ~~~~~
        
        Install the required modules:
        
        ::
        
            git submodule update --init --recursive
            virtualenv --python=/usr/bin/python3 .
            . bin/activate  # on bash
            source bin/activate.csh  # on csh
            pip install -r requirements.txt
            python -m spacy download en
            python ucca/setup.py install
            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 tupa/parse.py -t <train_dir> -d <dev_dir> -m <model_filename>
        
        To specify a model type (``sparse``, ``mlp`` or ``bilstm``), add
        ``-c <model_type>``.
        
        Parse a text file
        ~~~~~~~~~~~~~~~~~
        
        Run the parser on a text file (here named ``example.txt``) using a
        trained model:
        
        ::
        
            python tupa/parse.py example.txt -m <model_filename>
        
        A file named ``example.xml`` will be created.
        
        If you specified a model type using ``-c`` when training the model, be
        sure to include it when parsing too.
        
        Pre-trained models
        ~~~~~~~~~~~~~~~~~~
        
        To download and extract the pre-trained models, run:
        
        ::
        
            wget http://www.cs.huji.ac.il/~danielh/ucca/{sparse,mlp,bilstm}.tgz
            tar xvzf sparse.tgz
            tar xvzf mlp.tgz
            tar xvzf bilstm.tgz
        
        Run the parser using any of them:
        
        ::
        
            python tupa/parse.py example.txt -c sparse -m models/ucca-sparse
            python tupa/parse.py example.txt -c mlp -m models/ucca-mlp
            python tupa/parse.py example.txt -c bilstm -m models/ucca-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,
              title={A Transition-Based Directed Acyclic Graph Parser for {UCCA}},
              author={Hershcovich, Daniel and Abend, Omri and Rappoport, Ari},
              booktitle={Proc. of ACL},
              year={2017}
            }
        
        License
        -------
        
        This package is licensed under the GPLv3 or later license (see
        ```LICENSE.txt`` <LICENSE.txt>`__).
        
        .. |Build Status| image:: https://travis-ci.org/danielhers/tupa.svg?branch=master
           :target: https://travis-ci.org/danielhers/tupa
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3.6
Classifier: Topic :: Text Processing :: Linguistic
Classifier: License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)
