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        "author": "Vikash Singh",
        "author_email": "vikash.duliajan@gmail.com",
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            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.6",
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        "description": "This project has moved to `Flash Text <https://github.com/vi3k6i5/flashtext>`_. \n-----------------------------------------\n\nsynonym-extractor\n=================\n\nSynonym Extractor is a python library that is loosely based on `Aho-Corasick algorithm\n<https://en.wikipedia.org/wiki/Aho%E2%80%93Corasick_algorithm>`_.\n\nThe idea is to extract words that we care about from a given sentence in one pass.\n\nBasically say I have a vocabulary of 10K words and I want to get all the words from that set present in a sentence. A simple regex match will take a lot of time to loop over the 10K documents.\n\nHence we use a simpler yet much faster algorithm to get the desired result.\n\nInstallation\n-------\n::\n\n    pip install synonym-extractor\n\nUsage\n------\n::\n    \n    # import module\n    from synonym.extractor import SynonymExtractor\n\n    # Create an object of SynonymExtractor\n    synonym_extractor = SynonymExtractor()\n\n    # add synonyms\n    synonym_names = ['NY', 'new-york', 'SF']\n    clean_names = ['new york', 'new york', 'san francisco']\n\n    for synonym_name, clean_name in zip(synonym_names, clean_names):\n        synonym_extractor.add_to_synonym(synonym_name, clean_name)\n\n    synonyms_found = synonym_extractor.get_synonyms_from_sentence('I love SF and NY. new-york is the best.')\n\n    synonyms_found\n    >> ['san francisco', 'new york', 'new york']\n\n\nAlgorithm\n----------\n\nsynonym-extractor is based on `Aho-Corasick algorithm\n<https://en.wikipedia.org/wiki/Aho%E2%80%93Corasick_algorithm>`_.\n\nDocumentation\n----------\n\nDocumentation can be found at `Read the Docs\n<http://synonym-extractor.readthedocs.org>`_.\n\n\nWhy\n------\n\n::\n\nSay you have a corpus where similar words appear frequently.\n\neg: Last weekened I was in NY.\n    I am traveling to new york next weekend.\n\nIf you train a word2vec model on this or do any sort of NLP it will treat NY and new york as 2 different words. \n\nInstead if you create a synonym dictionary like:\n\neg: NY=>new york\n    new york=>new york\n\nThen you can extract NY and new york as the same text.\n\nTo do the same with regex it will take a lot of time:\n\n============  ========== = =========  ============\nDocs count    # Synonyms : Regex      synonym-extractor\n============  ========== = =========  ============\n1.5 million   2K         : 16 hours   NA\n2.5 million   10K        : 15 days    15 mins\n============  ========== = =========  ============\n\nThe idea for this library came from the following `StackOverflow question\n<https://stackoverflow.com/questions/44178449/regex-replace-is-taking-time-for-millions-of-documents-how-to-make-it-faster>`_.\n\n\nLicense\n-------\n\nThe project is licensed under the MIT license.",
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