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        "author": "Andreas van Cranenburgh",
        "author_email": "A.W.vanCranenburgh@uva.nl",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Environment :: Console",
            "Environment :: Web Environment",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: GNU General Public License (GPL)",
            "Operating System :: POSIX",
            "Programming Language :: Cython",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3.3",
            "Topic :: Text Processing :: Linguistic"
        ],
        "description": "=================\nDiscontinuous DOP\n=================\n\n.. image:: docs/images/disco-dop.png\n   :align: right\n   :alt: contrived discontinuous constituent for expository purposes.\n\nThe aim of this project is to parse discontinuous constituents in natural\nlanguage using Data-Oriented Parsing (DOP), with a focus on global world\ndomination. The grammar is extracted from a treebank of sentences annotated\nwith (discontinuous) phrase-structure trees. Concretely, this project provides\na statistical constituency parser with support for discontinuous constituents\nand Data-Oriented Parsing. Discontinuous constituents are supported through the\ngrammar formalism Linear Context-Free Rewriting System (LCFRS), which is a\ngeneralization of Probabilistic Context-Free Grammar (PCFG). Data-Oriented\nParsing allows re-use of arbitrary-sized fragments from previously seen\nsentences using Tree-Substitution Grammar (TSG).\n\n.. contents:: Contents of this README:\n   :local:\n\nFeatures\n========\nGeneral statistical parsing:\n\n- grammar formalisms: PCFG, PLCFRS\n- extract treebank grammar: trees decomposed into productions, relative\n  frequencies as probabilities\n- exact *k*-best list of derivations\n- coarse-to-fine pruning: posterior threshold,\n  *k*-best coarse-to-fine\n\nDOP specific (parsing with tree fragments):\n\n- implementations: Goodman's DOP reduction, Double-DOP, DOP1.\n- estimators: relative frequency estimate (RFE), equal weights estimate (EWE).\n- objective functions: most probable parse (MPP),\n  most probable derivation (MPD), most probable shortest derivation (MPSD),\n  most likely tree with shortest derivation (SL-DOP).\n- marginalization: n-best derivations, sampled derivations.\n\n.. image:: docs/images/runexp.png\n   :alt: screenshot of parse tree produced by parser\n\nInstallation\n============\n\nRequirements:\n\n- Python 3.3+     http://www.python.org (headers required, e.g. python3-dev package)\n- Cython 0.21+    http://www.cython.org\n- Numpy 1.5+      http://numpy.org/\n\nPython 2.7 is supported, but Python 3 is recommended.\nInstall the ``futures`` package when running Python 2.7.\n\nDebian, Ubuntu based systems\n----------------------------\nTo compile the latest development version on an `Ubuntu <http://www.ubuntu.com>`_ system,\nrun the following sequence of commands::\n\n    sudo apt-get install build-essential python3-dev python3-numpy python3-pip git\n    git clone --depth 1 git://github.com/andreasvc/disco-dop.git\n    cd disco-dop\n    pip3 install --user -r requirements.txt\n    make install\n\nThe ``--user`` option means the packages will be installed to your home\ndirectory which does not require root privileges. Make sure that\n``~/.local/bin`` directory is in your PATH, or add it as follows::\n\n    echo export PATH=$HOME/.local/bin:$PATH >> ~/.bashrc\n\nOther Linux systems\n-------------------\nThis assumes no root access, but assumes that ``gcc`` is installed.\n\nSet environment variables so that software can be installed to the home directory\n(replace with equivalent for your shell if you do not use bash)::\n\n    mkdir -p ~/.local\n    echo export PATH=$HOME/.local/bin:$PATH >> ~/.bashrc\n    echo export LD_LIBRARY_PATH=$HOME/.local/lib:/usr/lib64:/usr/lib >> ~/.bashrc\n    echo export PYTHONIOENCODING=\"utf-8\" >> ~/.bashrc\n\nAfter this, re-login or restart the shell to activate these settings.\nInstall Python 3 from source, if not installed already.\nPython may require some libraries such as ``zlib`` and ``readline``;\ninstallation steps are similar to the ones below::\n\n    wget http://www.python.org/ftp/python/3.5.1/Python-3.5.1.tgz\n    tar -xzf Python-*.tgz\n    cd Python-*\n    ./configure --prefix=$HOME/.local --enable-shared\n    make install && cd ..\n\nCheck by running ``python3`` that version 3.5.1 was installed successfully and\nis the default.\n\nInstall the latest development version of discodop::\n\n    wget https://github.com/andreasvc/disco-dop/archive/master.zip\n    unzip disco-dop-master.zip\n    cd disco-dop-master\n    pip install --user -r requirements.txt\n    make install\n\nMac OS X\n--------\n- Install `Xcode <https://developer.apple.com/>`_ and `Homebrew <http://brew.sh>`_\n- Install dependencies using Homebrew::\n\n    brew install gcc python3 git\n    git clone --depth 1 git://github.com/andreasvc/disco-dop.git\n    cd disco-dop\n    sudo pip3 install -r requirements.txt\n    env CC=gcc sudo python setup.py install\n    sudo make\n\nOther systems\n-------------\nIf you do not run Linux, it is possible to run the code inside a virtual machine.\nTo do that, install `Virtualbox <https://www.virtualbox.org/wiki/Downloads>`_\nand download the virtual machine imagine with disco-dop pre-installed:\nhttp://lang.science.uva.nl/VMs/discodop-vboximage.zip\n\n\nUsage, documentation\n====================\ndiscodop can be used in three ways:\n\n1. through the command line; cf. the manual pages for the ``discodop`` command\n   installed as part of the installation: ``man discodop``.\n2. as a library, cf. the `API reference <http://discodop.readthedocs.io/en/latest/api.html>`_\n   and `example notebooks <http://discodop.readthedocs.io/en/latest/intro.html#ipython-notebooks>`_\n3. `Web interfaces <http://discodop.readthedocs.io/en/latest/intro.html#web-interfaces>`_\n\nNB: avoid running discodop from within the source tree, to ensure that the\ninstalled versions of modules are imported.\n\nThe documentation can be found at http://discodop.readthedocs.io\n\nGrammars\n========\nCf. https://lang.science.uva.nl/grammars/\n\nThe English, German, and Dutch grammars are described in\n`van Cranenburgh et al., (2016) <http://dx.doi.org/10.15398/jlm.v4i1.100>`_;\nthe French grammar appears in `Sangati & van Cranenburgh (2015)\n<http://aclweb.org/anthology/W15-0902>`_.\nFor comparison, there is also an English grammar without discontinuous\nconstituents (``ptb-nodisc``).\n\nAcknowledgments\n===============\n\nThe Tree data structures in ``tree.py`` and the simple binarization algorithm\nin ``treetransforms.py`` were taken from `NLTK <http://www.nltk.org>`_.\nThe Zhang-Shasha tree-edit distance algorithm in ``treedist.py`` was taken from\nhttps://github.com/timtadh/zhang-shasha\nElements of the PLCFRS parser and punctuation re-attachment are based on code\nfrom `rparse <http://wolfgang-maier.de/rparse>`_. Various other bits inspired\nby the Stanford parser, Berkeley parser, Bubs parser, &c.\n\nReferences\n==========\nPlease cite the following paper if you use this code in the context of a\npublication::\n\n    @article{vancranenburgh2016disc,\n        title={Data-Oriented Parsing with discontinuous constituents and function tags},\n        author={van Cranenburgh, Andreas and Remko Scha and Rens Bod},\n        journal={Journal of Language Modelling},\n        year={2016},\n        volume={4},\n        number={1},\n        pages={57--111},\n        url={http://dx.doi.org/10.15398/jlm.v4i1.100}\n    }",
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