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    "info": {
        "author": "Marco Cornolti",
        "author_email": "cornolti@di.unipi.it",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "Intended Audience :: Information Technology",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: Apache Software License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.5",
            "Topic :: Scientific/Engineering :: Information Analysis",
            "Topic :: Software Development :: Libraries :: Python Modules",
            "Topic :: Text Processing :: Linguistic"
        ],
        "description": "============\ntagme-python\n============\n\nOfficial TagMe API wrapper for Python.\n\nInstallation and setup\n----------------------\n\nThis library is hosted by PyPI. You can install it with:\n\n``pip install tagme``\n\nTo access the TagMe API you have to register (for free!) at the D4Science platform and obtain an authorization *token*.\n\n- Register to the `D4Science TagMe VRE <https://services.d4science.org/group/tagme/>`_.\n- After login, click the *show* button on the left panel to get your authorization token.\n\nUsing TagMe\n-----------\n\nBefore making any call to the web service, you will need to set the module-wise ``GCUBE_TOKEN`` variable. You can do so with:\n\n.. code-block:: python\n\n import tagme\n # Set the authorization token for subsequent calls.\n tagme.GCUBE_TOKEN = \"<Your token goes here>\"\n\nAs an alternative to setting the module-wise variable, you can pass the token at each call with the optional ``gcube_token`` parameter.\n\nAnnotation\n----------\nThe annotation service lets you find entities mentioned in a text and link them to Wikipedia.\nThis is the so-called Sa2KB problem. You can annotate a text with:\n\n.. code-block:: python\n\n lunch_annotations = tagme.annotate(\"My favourite meal is Mexican burritos.\")\n\n # Print annotations with a score higher than 0.1\n for ann in lunch_annotations.get_annotations(0.1):\n     print ann\n\nThe ``annotate`` method accepts parameters to set the language (parameter ``lang``, that defaults to ``en``) and other stuff.\nSee the code for more information.\nAnnotations are associated a rho-score indicating the likelihood of an annotation being correct. In the example, we discard\nannotations with a score lower than 0.1.\n\nMention finding\n---------------\n\nThe mention finding service lets you find what parts of text may be a mention of an entity, without linking them to any entity.\n\n.. code-block:: python\n\n tomatoes_mentions = tagme.mentions(\"I definitely like ice cream better than tomatoes.\")\n\n for mention in tomatoes_mentions.mentions:\n     print mention\n\nThe ``mentions`` parameter accepts an optional language parameter ``lang`` that defaults to ``en``.\n\nEntity relatedness\n------------------\n\nTagme also gives you the semantic relatedness among pairs of entities. Entities can be either specified as Wikipedia titles\n(like ``Barack Obama``) or as Wikipedia IDs (like ``534366``, the ID of the entity Barack Obama).\nThe two methods for obtaining the relatedness among entities are ``relatedness_title`` (that accepts titles) and\n``relatedness_wid`` (that accepts Wikipedia IDs). Both methods accept either a single pair of entities or a list of pairs.\nYou can submit a list of pairs of any size, but the TagMe web service will be issued one query every 100 pairs.\nIf one entity does not exist, the result will be ``None``.\n\n.. code-block:: python\n\n # Get relatedness between a pair of entities specified by title.\n rels = tagme.relatedness_title((\"Barack Obama\", \"Italy\"))\n print \"Obama and italy have a semantic relation of\", rels.relatedness[0].rel\n\n # Get relatedness between a pair of entities specified by Wikipedia ID.\n rels = tagme.relatedness_wid((31717, 534366))\n print \"IDs 31717 and 534366 have a semantic relation of \", rels.relatedness[0].rel\n\n # Get relatedness between three pairs of entities specified by title.\n # The last entity does not exist, hence the value for that pair will be None.\n rels = tagme.relatedness_title([(\"Barack_Obama\", \"Italy\"),\n                                 (\"Italy\", \"Germany\"),\n                                 (\"Italy\", \"BAD ENTITY NAME\")])\n for rel in rels.relatedness:\n     print rel\n\n # You can also build a dictionary\n rels_dict = dict(rels)\n print rels_dict[(\"Barack Obama\", \"Italy\")]\n\nChangelog\n---------\n\nSee the `Changelog`_.\n\n.. _Changelog: CHANGELOG.rst",
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        "home_page": "https://github.com/marcocor/tagme-python",
        "keywords": "entity-linking nlp tagme api",
        "license": "Apache",
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        "name": "tagme",
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