Logistic Regression for NLP-brief

What we can do is to place the words in English in some high dimensional space. Then when we enter in the following in the system, "if i wake up late , i will miss the bus. and i wake up late"
Each of the words in that statement will have a polynomial associated with it, and when these polynomials combine to make the sentence they form what would otherwise be a decision boundary that contains the words "i miss the bus" which is the modus ponens conclusion of the above statement. Because the words exist in a high dimensional space it is easier to group words together as during training we will use gradient decent to move the words and adjust the polynomials so that they align. Once they do align , we can test the system with an arbitrary premise , and get a solution.

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