I am trying to run some experiments on internal word structure of morphologically rich language (Russian).Each valid character is 1-hot encoded and then fixed-length vectors are concatenated to produce word vector: c             a             t(0... 1... 0) (0... 1... 0) (0... 1... 0)This approach produces vectors of different length for words of different length. To unify them I have applied zero-padding:c a t 0 0 0 0 0 0 0 0k i t t e n 0 0 0 0 0t r a n q u i l i t yh a p p i n e s s 0 0Does this approach sounds valid? What are best practices on the described issue?
How to deal with word length variability while using char-level one-hot encoding?
neural network;deep learning