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        "author": "Francisco Canas",
        "author_email": "mailfrancisco@gmail.com",
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        "description": "Distiller\n=========\n\nDistiller provides convenient auto-extraction of document key words\nbased on term-frequency/inverse-document-frequency (TF-IDF) and word\npositioning.\n\nDistiller handles all of the pre-processing details and produces final\nstatistic reports in JSON format.\n\n\nRequirements\n------------\n\nDistiller uses the [Natural Language Toolkit](http://www.nltk.org/)\n\nYou will need to download a couple of NLTK packages:\n\n    >>> import nltk\n    >>> nltk.download()\n    Downloader> d\n    Download which package (l=list; x=cancel)?\n        Identifier> maxent_treebank_pos_tagger\n    Downloader> d\n    Download which package (l=list; x=cancel)?\n        Identifier> stopwords\n\n\n\nInstallation\n------------\n\nInstallation using pip:\n\n    $ pip install Distiller\n\n\nUsage\n-----\n\nTypical usage from within the Python interpreter:\n\n    >>> from Distiller.distiller import Distiller\n    >>> distiller = Distiller(data, target, options)\n\n\nArguments\n---------\n\n### data\n\nPath to file containing the document collection in JSON format.\n\n    {\n        'metadata': {\n            'base_url': 'The document's source URL (if any)'\n            },\n        'documents': [\n                {\n                    'id': 'The document's unique identifier (if any)',\n                    'body': 'The entire body of the document in a single text blob.',\n                }, ...\n            ]\n    }\n\n###target\n\nPath where Distiller will output the following reports:\n\nkeywords: A list of words and the frequency with which they were detected as being keywords of documents.\n\nbigrams: A list of word pairs and the frequency with which they were detected as being key pairs in documents.\n\ntrigrams: A list of word triples and the frequency with which they were detected as being key pairs in documents.\n\ndocmap: A mapping of document IDs to their respective keywords, n-grams, and other statistics.\n\nkeymap: A mapping of keywords to the documents they appear in.\n\n\n###options\n\nAn optional dictionary containing document processing arguments in this format:\n\n    {\n        'normalize': True,          # normalize tokens during pre processing\n        'stem': True,               # stems tokens during pre processing\n        'lemmatize': False,         # lemmatize during pre processing\n        'tfidf_cutoff': 0.001,      # cutoff value to use for term-freq/doc-freq score\n        'pos_list': ['NN','NNP'],   # POS white list used to filter for candidates\n        'black_list': []            # token list used to filter out from candidates\n    }",
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