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I am aware that LSI, RRI and word embeddings are distributional semantics models. However, I am not certain if the below mentioned are also distributional semantic models.

  • Non-Negative Tensor Factorisation
  • Singular Value Decomposition (SVD)
  • Vector Space Model (VSM)

Please let me know if the above mentioned algorithms are also distributional semantics models. Moreover, please also let me know the other distributional semantics based algorithms.

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The main idea here is: "birds of a feather flock together" That is, words that appear near each other inform the "function" of a word.

More importantly, I think of the techniques you mentioned as "methods," rather than "models." The reason why is it seems possible to violate the definition of what a distributional semantic model is without appropriate data preprocessing.

SVD for example, is typically a dimensionality reduction technique, or the pre-cursor to a clustering technique depending on feature engineering and usage of the methodology. In this case, if you were counting the co-occurrence of words across documents -- rows = documents, cols = words, cells = # of times that word appeared in that particular document -- and then you ran SVD on that, you might have the pre-cursor to a "model" that may eventually be something you can call distributional semantic.

Another example could be Word2Vec, where one typically uses a neural network to train a shallow layer, and extract the weights. Word2Vec can be trained via Skip-gram, or continuous bag of words. Since the "meaning" of a word is derived from the co-occurrence and/or proximity to neighboring words, it may be considered a distributional semantic model. FastText is probably even more so, since it explicitly uses the distribution of words in documents to perform a similar vector operation.

Latent Dirichlet Allocation may be another example. Perhaps even Naive Bayes, if used in the appropriate manner.

So ultimately, the answer is, it depends on data pre-processing/feature-engineering and usage, rather than the technique.

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