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Package for working with semantic spaces.

Project 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':

from import SemanticSpace

space = SemanticSpace.from_csv('space.w2v.gz')


{'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.


pip install -r requirements.txt
python install

Semantic spaces

You can download a set of validated semantic spaces for English and Dutch here (see Mandera, Keuleers, & Brysbaert, in press).



The tool was developed at Center for Reading Research, Ghent University by Paweł Mandera.


The project is licensed under the Apache License 2.0.


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.

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