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        "author": "ZhangDun",
        "author_email": "dunnzhang0@gmail.com",
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            "Development Status :: 2 - Pre-Alpha",
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            "License :: OSI Approved :: MIT License",
            "Operating System :: MacOS",
            "Operating System :: Microsoft :: Windows",
            "Operating System :: POSIX",
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            "Programming Language :: Python :: 3",
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        "description": "DomainThesaurus\n================\n\nIntroduction\n------------\n\n**DomainThesaurus** is a python package offering a technique of extracting domain-specific\nthesaurus which is commonly used in *Natural Language Processing*. Here is one item of generated\nthesaurus::\n\n    { \"internet explorer\":\n    {\"abbreviation\":[\"ie\"],\n    \"synonym\":[\"internet explorers\", \"internet explorere\", \"internetexplorer\"],\n    \"other\":[\"firefox\",\"chrome\",\"opera\"]}\n    }\n\nExcept for domain-specific thesaurus, the package also provides several useful modules.\nFor example, **DomainTerm** for extracting domain-specific term and **WordDiscrimination**\nfor discriminate words (e.g. abbreviation, synonyms).\n\nDomain-Specific term\n::::::::::::::::::::::::::::::\n\n**DomainTerm** can automatically extract domain-specific terms from domain corpus.\nFor example, *Javascript* in the domain of  computer science and technology and *karush kuhn tucker* in\ndomain of mathematics.\n\nAbbreviations and Synonyms\n:::::::::::::::::::::::::::\n\nThe module **WordDiscrimination** can divide semantic-related words into different types.\nThe default module can recognize semantic-related words as `abbreviation` and `synonym`. Note that,\nin our module, the `synonym` means that two words are semantic-related word and they are morphologically similar.\nFor example, *ie* is the abbreviation of *internet explorer* and *javascripts* is\nthe synonym of *javascript*.\n\nInstallation\n------------\n\n**DomainThesaurus** is tested to work under `Python 3.x`. Please use it in `Python 3.x`.\nWe will try to support *Python 2.x*.\n\nDependency requirements:\n\n* gensim(>=3.6.0)\n* networkx(>=2.1)\n\n**DomainThesaurus** is currently available on the PyPi's repository and you can\ninstall it via `pip`::\n\n  pip install DomainThesaurus\n\nIf you prefer, you can clone it and run the setup.py file. Use the following\ncommand to get a copy from GitHub::\n\n git clone https://github.com/DunZhang/DomainSpecificThesaurus.git\n\n\nUsage\n----------\n\nA simple example::\n    >>> dst = DomainThesaurus(domain_specific_corpus_path=\"your domain specific corpus path\",\n    >>>                       general_vocab_path=\"your general vocab path\",\n    >>>                       outputDir=\"path of output\")\n    >>> # extract domain thesauruss\n    >>> your_thesaurus = dst.extract()\n\nIf you don't have any datasets, you can copy and run this code:\nhttps://github.com/DunZhang/DomainSpecificThesaurus/blob/master/docs/notebooks/domain_thesaurus.ipynb .\nThis code will automatically download datasets for you.\nThe code design is flexible, you can replace the default `function class` with your own `function class` to get better\nperformance.\nYou can find more usage in https://github.com/DunZhang/DomainSpecificThesaurus/blob/master/docs/notebooks\n\nAcknowledgements\n-----------------\n\nIn this project, we use `Levenshtein Distance` and `GoogleDriveDownloader` from https://pypi.org/project/jellyfish/\nand  https://github.com/ndrplz/google-drive-downloader. Thanks for their code.\n",
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