Metadata-Version: 2.1
Name: semspaces
Version: 0.1.5
Summary: Package for working with semantic spaces.
Home-page: https://github.com/pmandera/semspaces/
Author: Paweł Mandera
Author-email: pawel@pawelmandera.com
License: UNKNOWN
Description: # Semantic spaces module
        
        This is a python module that allows to compute semantic metrics based on
        distributional semantics models.
        
        For example, to find words that are semantically similar to the word 'brain':
        
        ```python
        from semspaces.space import SemanticSpace
        
        space = SemanticSpace.from_csv('space.w2v.gz')
        
        space.most_similar(['brain'])
        
        {'brain': [(u'brain', 0.0),
          (u'brains', 0.34469844325620635),
          (u'cerebrum', 0.4426992023455152),
          (u'cerebellum', 0.4483798859566903),
          (u'cortical', 0.469348588934828),
          (u'brainstem', 0.4791188497952641),
          (u'cortex', 0.479544888313173),
          (u'ganglion', 0.49717579235842546),
          (u'thalamus', 0.5030885466349713),
          (u'thalamic', 0.5059524199702277)]}
        ```
        
        The module wraps dense and sparse matrix implementations to provide convenience
        methods for computing semantic statistics as well as easy input and output of
        the data.
        
        # Installation
        
        ```bash
        pip install -r requirements.txt
        python setup.py install
        ```
        
        # Semantic spaces
        
        You can download a set of validated semantic spaces for English and Dutch
        [here](http://zipf.ugent.be/snaut/spaces/) (see Mandera, Keuleers, & Brysbaert,
        in press). 
        
        # Contribute 
        
        - Issue Tracker: https://github.com/pmandera/semspaces/issues
        - Source Code: https://github.com/pmandera/semspaces
        
        # Authors
        
        The tool was developed at Center for Reading Research, Ghent University by
        [Paweł Mandera](http://crr.ugent.be/pawel-mandera).
        
        # License
        
        The project is licensed under the Apache License 2.0.
        
        # References
        
        Mandera, P., Keuleers, E., & Brysbaert, M. (in press). Explaining human
        performance in psycholinguistic tasks with models of semantic similarity based
        on prediction and counting: A review and empirical validation. *Journal of
        Memory and Language*.
        
Keywords: semantic space word vectors
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: Apache Software License
Description-Content-Type: text/markdown
