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            "License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
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            "Programming Language :: Python :: 2.7",
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            "Topic :: Text Processing :: Linguistic"
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        "description": "Colibri Core\n================\n\n.. image:: https://travis-ci.org/proycon/colibri-core.svg?branch=master\n    :target: https://travis-ci.org/proycon/colibri-core\n\n.. image:: https://badge.fury.io/py/colibricore.svg\n    :target: http://badge.fury.io/py/colibricore\n\n.. image:: https://zenodo.org/badge/12996232.svg\n   :target: https://zenodo.org/badge/latestdoi/12996232\n\n.. image:: http://applejack.science.ru.nl/lamabadge.php/colibri-core\n   :target: http://applejack.science.ru.nl/languagemachines/\n\n*by Maarten van Gompel, proycon@anaproy.nl, Radboud University Nijmegen*\n\n*Licensed under GPLv3 (See http://www.gnu.org/licenses/gpl-3.0.html)*\n\n\nColibri Core is software to quickly and efficiently count and extract patterns\nfrom large corpus data, to extract various statistics on the extracted\npatterns, and to compute relations between the extracted patterns. The employed\nnotion of pattern or construction encompasses the following categories:\n\n* **n-gram** -- *n* consecutive words\n* **skipgram** -- An abstract pattern of predetermined length with one or multiple gaps (of specific size).\n* **flexgram** -- An abstract pattern with one or more gaps of variable-size.\n\nN-gram extraction may seem fairly trivial at first, with a few lines in your\nfavourite scripting language, you can move a simple sliding window of size **n**\nover your corpus and store the results in some kind of hashmap. This trivial\napproach however makes an unnecessarily high demand on memory resources, this\noften becomes prohibitive if unleashed on large corpora. Colibri Core tries to\nminimise these space requirements in several ways:\n\n* **Compressed binary representation** -- Each word type is assigned a numeric class, which is encoded in a compact binary format in which highly frequent classes take less space than less frequent classes. Colibri core always uses this representation rather than a full string representation, both on disk and in memory.\n* **Informed iterative counting** -- Counting is performed more intelligently by iteratively processing the corpus in several passes and quickly discarding patterns that won't reach the desired occurrence threshold.\n\nSkipgram and flexgram extraction are computationally more demanding but have\nbeen implemented with similar optimisations. Skipgrams are computed by\nabstracting over n-grams, and flexgrams in turn are computed either by\nabstracting over skipgrams, or directly from n-grams on the basis of\nco-occurrence information (mutual pointwise information).\n\nAt the heart of the sofware is the notion of pattern models. The core tool, to\nbe used from the command-line, is ``colibri-patternmodeller`` which enables you\nto build pattern models, generate statistical reports, query for specific\npatterns and relations, and manipulate models.\n\nA pattern model is simply a collection of extracted patterns (any of the three\ncategories) and their counts from a specific corpus. Pattern models come in two\nvarieties:\n\n* **Unindexed Pattern Model** -- The simplest form, which simply stores the patterns and their count.\n* **Indexed Pattern Model** -- The more informed form, which retains all indices to the original corpus, at the cost of more memory/diskspace.\n\nThe Indexed Pattern Model is much more powerful, and allows more statistics and\nrelations to be inferred.\n\nThe generation of pattern models is optionally parametrised by a minimum\noccurrence threshold, a maximum pattern length, and a lower-boundary on the\ndifferent types that may instantiate a skipgram (i.e. possible fillings of the\ngaps).\n\nTechnical Details\n--------------------\n\nColibri Core is available as a collection of **standalone command-line tools**,\nas a **C++ library**, and as a **Python library**.\n\nPlease consult the full documentation at https://proycon.github.io/colibri-core\n\nInstallation instructions are here:  https://proycon.github.io/colibri-core/doc/#installation\n\nPublication\n----------------------\n\nThis software is extensively described in the following peer-reviewed publication:\n\n van Gompel, M and van den Bosch, A (2016)\n Efficient n-gram, Skipgram and Flexgram Modelling with Colibri Core.\n *Journal of Open Research Software*\n 4: e30, DOI: http://dx.doi.org/10.5334/jors.105\n\nClick the link to access the publication and please cite it if you make use of\nColibri Core in your work.",
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