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"description": "PyStanfordDependencies\n======================\n\n.. image:: https://travis-ci.org/dmcc/PyStanfordDependencies.svg?branch=master\n :target: https://travis-ci.org/dmcc/PyStanfordDependencies\n\n.. image:: https://badge.fury.io/py/PyStanfordDependencies.png\n :target: https://badge.fury.io/py/PyStanfordDependencies\n\n.. image:: https://coveralls.io/repos/dmcc/PyStanfordDependencies/badge.png?branch=master\n :target: https://coveralls.io/r/dmcc/PyStanfordDependencies?branch=master\n\nPython interface for converting `Penn Treebank\n`_ trees to `Universal\nDependencies `_\nand `Stanford Dependencies\n`_.\n\nExample usage\n-------------\nStart by getting a ``StanfordDependencies`` instance with\n``StanfordDependencies.get_instance()``::\n\n >>> import StanfordDependencies\n >>> sd = StanfordDependencies.get_instance(backend='subprocess')\n\n``get_instance()`` takes several options. ``backend`` can currently\nbe ``subprocess`` or ``jpype`` (see below). If you have an existing\n`Stanford CoreNLP `_ or\n`Stanford Parser `_\njar file, use the ``jar_filename`` parameter to point to the full path of\nthe jar file. Otherwise, PyStanfordDependencies will download a jar file\nfor you and store it in locally (``~/.local/share/pystanforddeps``). You\ncan request a specific version with the ``version`` flag, e.g.,\n``version='3.4.1'``. To convert trees, use the ``convert_trees()`` or\n``convert_tree()`` method (note that by default, ``convert_trees()`` can\nbe considerably faster if you're doing batch conversion). These return\na sentence (list of ``Token`` objects) or a list of sentences (list of\nlist of ``Token`` objects) respectively::\n\n >>> sent = sd.convert_tree('(S1 (NP (DT some) (JJ blue) (NN moose)))')\n >>> for token in sent:\n ... print token\n ...\n Token(index=1, form='some', cpos='DT', pos='DT', head=3, deprel='det')\n Token(index=2, form='blue', cpos='JJ', pos='JJ', head=3, deprel='amod')\n Token(index=3, form='moose', cpos='NN', pos='NN', head=0, deprel='root')\n\nThis tells you that ``moose`` is the head of the sentence and is\nmodified by ``some`` (with a ``det`` = determiner relation) and ``blue``\n(with an ``amod`` = adjective modifier relation). Fields on ``Token``\nobjects are readable as attributes. See docs for additional options in\n``convert_tree()`` and ``convert_trees()``.\n\nVisualization\n-------------\n\nIf you have the `asciitree `_\npackage, you can use a prettier ASCII formatter::\n\n >>> print sent.as_asciitree()\n moose [root]\n +-- some [det]\n +-- blue [amod]\n\nIf you have Python 2.7 or later, you can use `Graphviz\n`_ to render your graphs. You'll need the `Python\ngraphviz `_ package to call\n``as_dotgraph()``::\n\n >>> dotgraph = sent.as_dotgraph()\n >>> print dotgraph\n digraph {\n 0 [label=root]\n 1 [label=some]\n 3 -> 1 [label=det]\n 2 [label=blue]\n 3 -> 2 [label=amod]\n 3 [label=moose]\n 0 -> 3 [label=root]\n }\n >>> dotgraph.render('moose') # renders a PDF by default\n 'moose.pdf'\n >>> dotgraph.format = 'svg'\n >>> dotgraph.render('moose')\n 'moose.svg'\n\nThe Python `xdot `_\npackage provides an interactive visualization::\n\n >>> import xdot\n >>> window = xdot.DotWindow()\n >>> window.set_dotcode(dotgraph.source)\n\nBoth ``as_asciitree()`` and ``as_dotgraph()`` allow customization.\nSee the docs for additional options.\n\nBackends\n--------\nCurrently PyStanfordDependencies includes two backends:\n\n- ``subprocess`` (works anywhere with a ``java`` binary, but more\n overhead so batched conversions with ``convert_trees()`` are\n recommended)\n- ``jpype`` (requires `jpype1 `_,\n faster than the subprocess backend, also includes access to the Stanford\n CoreNLP lemmatizer)\n\nBy default, PyStanfordDependencies will attempt to use the ``jpype``\nbackend. If ``jpype`` isn't available or crashes on startup,\nPyStanfordDependencies will fallback to ``subprocess`` with a warning.\n\nUniversal Dependencies status\n-----------------------------\nPyStanfordDependencies supports most features in `Universal Dependencies\n`_ (see `issue #10\n`_ for the\nmost up to date status). PyStanfordDependencies output matches Universal\nDependencies in terms of structure and dependency labels, but Universal\nPOS tags and features are missing. Currently, PyStanfordDependencies will\noutput Universal Dependencies by default (unless you're using Stanford\nCoreNLP 3.5.1 or earlier).\n\nRelated projects\n----------------\n- `clearnlp-converter `_\n (uses `clearnlp `_ instead of `Stanford\n CoreNLP `_ for\n dependency conversion)\n\nMore information\n----------------\nLicensed under `Apache 2.0 `_.\n\nWritten by David McClosky (`homepage\n`_, `code `_)\n\nBug reports and feature requests: `GitHub issue tracker\n`_\n\nRelease summaries\n-----------------\n- 0.3.1 (2015.11.02): Better collapsed universal handling, bugfixes\n- 0.3.0 (2015.10.09): Support copy nodes, more input checking/debugging\n help, example ``convert.py`` program\n- 0.2.0 (2015.08.02): Universal Dependencies support (mostly),\n Python 3 support (fully), minor API updates\n- 0.1.7 (2015.06.13): Bugfixes for ``JPype``, handle version mismatches\n in IBM Java\n- 0.1.6 (2015.02.12): Support for ``graphviz`` formatting, CoreNLP 3.5.1,\n better Windows portability\n- 0.1.5 (2015.01.10): Support for ASCII tree formatting\n- 0.1.4 (2015.01.07): Fix ``CCprocessed`` support\n- 0.1.3 (2015.01.03): Bugfixes, coveralls integration, refactoring\n- 0.1.2 (2015.01.02): Better CoNLL structures, test suite and Travis CI\n support, bugfixes\n- 0.1.1 (2014.12.15): More docs, fewer bugs\n- 0.1 (2014.12.14): Initial release",
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