Getting wrong ch2 values from sklearn chi2
I don't know what is your source that said that value of chi-square should be between 0 and 1. Imagine as in that equation in the picture say two terms t and c always co-occur, meaning D(t|~c)=0 (number of documents with no c but t in them) and D(c|~t)=0 (number of documents with c in them but no t). This means the equation in the snap reduces down to 

$\chi^2(t, c) = N*(AD)^2/A^2D^2 = N$.

Edit 1:

After reading your comment, I understood you are confused regarding the 'normalised' part. A normalised value does not always mean a scaled value between 0 and 1. Normalised value does mean a value that is scaled appropriately for comparing and in my opinion, the denominator gives that comparable effect. The type of normalisation that you are associating this situation to is feature scaling. Check out normalization.