Metadata-Version: 2.1
Name: wrapperCoreference
Version: 0.0.1
Summary: Coreference Resolution wrapper
Home-page: UNKNOWN
Author: Henry Rosales
Author-email: hrosmendez@gmail.com
License: UNKNOWN
Description: # Coreference Resolution wrapper
        
        Coreference Resolution is the task of finding all expressions that refer to the same entity in a text. It is an important step for a lot of higher level NLP tasks that involve natural language understanding such as document summarization, question answering, and information extraction.
        
        This is a simple library that wrap two Coreference Resolution models form StanfordNLP package: the statistic and neural models. We use here the SpaCy package to load the neural model (a.k.a, *NeuralCoref*), and the stanfordnlp package to load the statistic model (a.k.a, *CoreNLPCoref*).
        
        ## Requirements
        
        ```bash
        pip3 install spacy
        pip3 install stanfordnlp
        pip3 install wrapperCoreference
        ```
        
        StanfordNLP also require the manual downloading of a core of modules, review [here](https://stanfordnlp.github.io/CoreNLP/download.html) for more details.
        
        ```bash
        wget http://nlp.stanford.edu/software/stanford-corenlp-full-2018-10-05.zip
        ```
        
        ## Methods
        Example of usage of the neural model 
        ```python
        from wrapperCoreference import WrapperCoreference
        wc = WrapperCoreference()
        wc.NeuralCoref(u'My sister has a dog. She loves him.')
        #output: [{'start': 21, 'end': 24, 'text': 'She', 'resolved': 'My sister'}, {'start': 31, 'end': 34, 'text': 'him', 'resolved': 'a dog'}]
        ```
        
        
        Example of usage of the statistic model 
        ```python
        from wrapperCoreference import WrapperCoreference
        wc = WrapperCoreference()
        wc.setCoreNLP('/tmp/stanford-corenlp-full-2018-10-05')
        print(wc.CoreNLPCoref(u'My sister has a dog. She loves him.'))
        #output: [{'start': 31, 'end': 34, 'text': 'him', 'resolved': 'a dog', 'fullInformation': [{'start': 14, 'end': 19, 'text': 'a dog'}]}, {'start' : 21, 'end': 24, 'text': 'She', 'resolved': 'My sister', 'fullInformation': [{'start': 0, 'end': 9, 'text': 'My sister'}]}]
        ```
        
Keywords: Coreference Resolution,NLP
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Education
Classifier: Topic :: Software Development :: Build Tools
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Requires-Python: >=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, <4
Description-Content-Type: text/markdown
Provides-Extra: dev
Provides-Extra: test
