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  • $\begingroup$ That's all clear. What I don't understand is why the learning process produces good word vectors. Gradient descent trains the whole model and it just so happens that the first part of the model produces word vectors with a wonderfully benevolent quality (similarity as mentioned above). Why? $\endgroup$ Commented Aug 2, 2022 at 7:03
  • $\begingroup$ The weights are distributed thanks to hyperparameters (mainly window size and learning rate) and the good results probably come from many trials and errors, like many DL models. If you want to know how it works precisely step by step, you can look this notebook. github.com/chiaminchuang/A-Neural-Probabilistic-Language-Model/… $\endgroup$ Commented Aug 2, 2022 at 14:07
  • $\begingroup$ Does it answer your question? Please let me know if you need more details. $\endgroup$ Commented Aug 3, 2022 at 15:32
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    $\begingroup$ This doesn't answer my question, unfortunately. I'm still gathering information and reading a few papers such as the spiritual successors to the NNLM above (Word2Vec, Glove, ..). I assume to find relevant information about my question there. $\endgroup$ Commented Aug 5, 2022 at 6:39
  • $\begingroup$ Sorry for that, and please let me know if you find any relevant answers to your question. Although a mathematical understanding might be possible, many DL topics start with a conceptual idea that is improved through trials and errors, rather than rigorous logic. I might be wrong for NNLM, but the best way to understand such an algorithm is to ask authors themselves or redo it. $\endgroup$ Commented Aug 5, 2022 at 7:31