Finding Neural Network input parameters for classifying text documents

Question

I need to train a neural network to classify some text documents into a boolean class (NN has one output with "Yes" or "No" values).
Is there any algorithm to find best input parameters (for example presence of words, term, sentence and/or frequency/repetition of a word & ...) ?
If not can you give me a starting point to find these parameters(How should I select them)?

Thanks

Answer 1

The standard approach I know of would be to use a vector of words/terms and assign them a negative or positive score using a learning or statistical algorithm. even perceptron learning should suffice, you just need a good set of positive and negative examples.

To my knoledge this the way all spam filter work. and they work pretty well.