Metadata-Version: 1.2
Name: pyconll
Version: 2.3.1
Summary: Read and manipulate CoNLL files
Home-page: https://github.com/pyconll/pyconll
Author: Matias Grioni
Author-email: matgrioni@gmail.com
License: MIT
Description: |Build Status| |Coverage Status| |Documentation Status| |gitter|
        
        pyconll
        -------
        
        *Easily work with **CoNLL** files using the familiar syntax of
        **python**.*
        
        Links
        '''''
        
        -  `Homepage <https://pyconll.github.io>`__
        -  `Documentation <https://pyconll.readthedocs.io/>`__
        
        Installation
        ~~~~~~~~~~~~
        
        As with most python packages, simply use ``pip`` to install from PyPi.
        
        ::
        
            pip install pyconll
        
        ``pyconll`` is also available as a conda package on the ``pyconll``
        channel. Only package 2.2.0 and newer are available for conda at the
        moment.
        
        ::
        
            conda install -c pyconll pyconll
        
        This package is designed for, and only tested with python 3.4 and up and
        will not be backported to python 2.x or to any versions older than
        python 3.4 as this release has reached end of support in 2019.
        
        Motivation
        ~~~~~~~~~~
        
        This tool is intended to be a **minimal**, **low level**, and
        **functional** library in a widely used programming language. pyconll
        creates a thin API on top of raw CoNLL annotations that is simple and
        intuitive in a popular programming language.
        
        In my work with the Universal Dependencies project, I saw a
        dissapointing lack of low level APIs for working with the CoNLL-U
        format. Most tooling focuses on graph transformations and DSLs for
        terse, automated changes. Tools such as `Grew <http://grew.fr/>`__ and
        `Treex <http://ufal.mff.cuni.cz/treex>`__ are very powerful and
        productive, but have a learning curve and are limited the scope of their
        DSLs. `CL-CoNLLU <https://github.com/own-pt/cl-conllu/>`__ is simple and
        low level, but Common Lisp is not widely used in NLP, and difficult to
        pickup for beginners. `UDAPI <http://udapi.github.io/>`__ is in python
        but it is very large and has little guidance. pyconll attempts to fill
        the gaps between what other projects have accomplished.
        
        Other useful tools can be found on the Universal Dependencies
        `website <https://universaldependencies.org/tools.html>`__.
        
        Hopefully, individual researchers find pyconll useful, and will use it
        as a building block for their tools and projects. pyconll affords a
        standardized and complete base for building larger projects without
        worrying about CoNLL annotation and output.
        
        Code Snippet
        ~~~~~~~~~~~~
        
        .. code:: python
        
            # This snippet finds what lemmas are marked as AUX which is a closed class POS in UD
            import pyconll
        
            UD_ENGLISH_TRAIN = './ud/train.conll'
        
            train = pyconll.load_from_file(UD_ENGLISH_TRAIN)
        
            aux_lemmas = set()
            for sentence in train:
                for token in sentence:
                    if token.upos == 'AUX':
                        aux_lemmas.add(token.lemma)
        
        Uses and Limitations
        ~~~~~~~~~~~~~~~~~~~~
        
        This package edits CoNLL-U annotations. This does not include the
        annotated text itself. Word forms on Tokens are not editable and
        Sentence Tokens cannot be reassigned or reordered. ``pyconll`` focuses
        on editing CoNLL-U annotation rather than creating it or changing the
        underlying text that is annotated. If there is interest in this
        functionality area, please create a github issue for more visibility.
        
        This package also is only validated against the CoNLL-U format. The
        CoNLL and CoNLL-X format are not supported, but are very similar. I
        originally intended to support these formats as well, but their format
        is not as well defined as CoNLL-U so they are not included. Please
        create an issue for visibility if this feature interests you.
        
        Lastly, linguistic data can often be very large and this package
        attempts to keep that in mind. pyconll provides methods for creating in
        memory conll objects along with an iterate only version in case a corpus
        is too large to store in memory (the size of the memory structure is
        several times larger than the actual corpus file). The iterate only
        version can parse upwards of 100,000 words per second on a 16gb ram
        machine, so for most datasets to be used on a dev machine, this package
        will perform well. The 2.2.0 release also improves parse time and memory
        footprint by about 25%!
        
        Contributing
        ~~~~~~~~~~~~
        
        Contributions to this project are welcome and encouraged! If you are
        unsure how to contribute, here is a
        `guide <https://help.github.com/en/articles/creating-a-pull-request-from-a-fork>`__
        from Github explaining the basic workflow. After cloning this repo,
        please run ``make hooks`` and ``pip install -r requirements.txt`` to
        properly setup locally. ``make hooks`` setups up a pre-push hook to
        validate that code matches the default YAPF style. While this is
        technically optional, it is highly encouraged.
        ``pip install -r requirements.txt`` sets up environment dependencies
        like ``yapf``, ``twine``, ``sphinx``, etc.
        
        For packaging new versions, please use setuptools version 24.2.0 or
        greater for creating the appropriate packaging that recognizes the
        ``python_requires`` metadata.
        
        README and CHANGELOG
        ^^^^^^^^^^^^^^^^^^^^
        
        When changing either of these files, please change the Markdown version
        and run ``make docs`` so that the other versions stay in sync.
        
        Code Formatting
        ^^^^^^^^^^^^^^^
        
        Code formatting is done automatically on push if githooks are setup
        properly. The code formatter is
        `YAPF <https://github.com/google/yapf>`__, and using this ensures that
        coding style stays consistent over time and between authors.
        
        .. |Build Status| image:: https://travis-ci.org/pyconll/pyconll.svg?branch=master
           :target: https://travis-ci.org/pyconll/pyconll
        .. |Coverage Status| image:: https://coveralls.io/repos/github/pyconll/pyconll/badge.svg?branch=master
           :target: https://coveralls.io/github/pyconll/pyconll?branch=master
        .. |Documentation Status| image:: https://readthedocs.org/projects/pyconll/badge/?version=stable
           :target: https://pyconll.readthedocs.io/en/latest/?badge=latest
        .. |gitter| image:: https://badges.gitter.im/pyconll/pyconll.svg
           :target: https://gitter.im/pyconll/pyconll?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge
        
Keywords: nlp,conllu,conll,universal dependencies
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Utilities
Requires-Python: ~=3.4
