Metadata-Version: 1.1
Name: tagme
Version: 0.1.3
Summary: Official TagMe API wrapper for Python
Home-page: https://github.com/marcocor/tagme-python
Author: Marco Cornolti
Author-email: cornolti@di.unipi.it
License: Apache
Description: ============
        tagme-python
        ============
        
        Official TagMe API wrapper for Python.
        
        Installation and setup
        ----------------------
        
        This library is hosted by PyPI. You can install it with:
        
        ``pip install tagme``
        
        To access the TagMe API you have to register (for free!) at the D4Science platform and obtain an authorization *token*.
        
        - Register to the `D4Science TagMe VRE <https://services.d4science.org/group/tagme/>`_.
        - After login, click the *show* button on the left panel to get your authorization token.
        
        Using TagMe
        -----------
        
        Before making any call to the web service, you will need to set the module-wise ``GCUBE_TOKEN`` variable. You can do so with:
        
        .. code-block:: python
        
         import tagme
         # Set the authorization token for subsequent calls.
         tagme.GCUBE_TOKEN = "<Your token goes here>"
        
        As an alternative to setting the module-wise variable, you can pass the token at each call with the optional ``gcube_token`` parameter. 
        
        Annotation
        ----------
        The annotation service lets you find entities mentioned in a text and link them to Wikipedia.
        This is the so-called Sa2KB problem. You can annotate a text with:
        
        .. code-block:: python
        
         lunch_annotations = tagme.annotate("My favourite meal is Mexican burritos.")
         
         # Print annotations with a score higher than 0.1
         for ann in lunch_annotations.get_annotations(0.1):
             print ann
        
        The ``annotate`` method accepts parameters to set the language (parameter ``lang``, that defaults to ``en``) and other stuff.
        See the code for more information.
        Annotations are associated a rho-score indicating the likelihood of an annotation being correct. In the example, we discard
        annotations with a score lower than 0.1.
        
        Mention finding
        ---------------
        
        The mention finding service lets you find what parts of text may be a mention of an entity, without linking them to any entity.
        
        .. code-block:: python
        
         tomatoes_mentions = tagme.mentions("I definitely like ice cream better than tomatoes.")
        
         for mention in tomatoes_mentions.mentions:
             print mention
        
        The ``mentions`` parameter accepts an optional language parameter ``lang`` that defaults to ``en``.
        
        Entity relatedness
        ------------------
        
        Tagme also gives you the semantic relatedness among pairs of entities. Entities can be either specified as Wikipedia titles
        (like ``Barack Obama``) or as Wikipedia IDs (like ``534366``, the ID of the entity Barack Obama).
        The two methods for obtaining the relatedness among entities are ``relatedness_title`` (that accepts titles) and
        ``relatedness_wid`` (that accepts Wikipedia IDs). Both methods accept either a single pair of entities or a list of pairs.
        You can submit a list of pairs of any size, but the TagMe web service will be issued one query every 100 pairs.
        If one entity does not exist, the result will be ``None``.
        
        .. code-block:: python
        
         # Get relatedness between a pair of entities specified by title.
         rels = tagme.relatedness_title(("Barack Obama", "Italy"))
         print "Obama and italy have a semantic relation of", rels.relatedness[0].rel
         
         # Get relatedness between a pair of entities specified by Wikipedia ID.
         rels = tagme.relatedness_wid((31717, 534366))
         print "IDs 31717 and 534366 have a semantic relation of ", rels.relatedness[0].rel
         
         # Get relatedness between three pairs of entities specified by title.
         # The last entity does not exist, hence the value for that pair will be None.
         rels = tagme.relatedness_title([("Barack_Obama", "Italy"),
                                         ("Italy", "Germany"),
                                         ("Italy", "BAD ENTITY NAME")])
         for rel in rels.relatedness:
             print rel
        
         # You can also build a dictionary
         rels_dict = dict(rels)
         print rels_dict[("Barack_Obama", "Italy")]
         
         
        
Keywords: entity-linking nlp tagme api
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Information Technology
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Text Processing :: Linguistic
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
