Distributional Semantic Modeling System

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A Distributional Semantic Modeling System is a data-driven semantic modeling system/data-driven word vectorizing system that can solve a distributional word vectorizing function training task by implementing a Distributional Semantic Modeling Algorithm.



References

2014

  • http://www.marekrei.com/blog/linguistic-regularities-word-representations/
    • As the first step, we need to create feature vectors for each word in our vocabulary. The two main ways of doing this, which are also considered by this paper, are:
      • BOW: The bag-of words approach. We count all the words that a certain word appears with, treat the counts as features in a vector, and weight them using something like positive pointwise mutual information (PPMI).
      • word2vec: Vectors created using the word2vec toolkit. Low-dimensional dense vector representations are learned for each word using a neural network.
    • If you want to learn more details about these models, take a look at an earlier post about comparing both of these models.

2010