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I have a questionnaire consisting with over 10 questions. The questionnaire is being answered by a lot of people - which I have manually graded. Each question can give the user up to 10 points depending on how they have answered. Let's say that my dataset is big enough, how would I go about using a Neural Network to automatically grade these questions for me?

I have used Neural Networks (CNN) before in relation to image classifying. But when dealing with text classifying, where should I start? Is there some sort of tutorial out there that covers this with a similar example?

Thanks in advance.

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use the embedding layers which are mainly used for text.

input the question number and the text that the student wrote to the algorithm. Make the problem into a regression of a number from 0 to 10 ( or a classification of class 10, see which one gives better performance). So the NN will look at text given a question number and try to figure out which 'points' should it get. It will be a supervised training problem since you already have examples of text rated from 0 to 10.

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