For supervised sequence-labeling problems in NLP, the most important "complicating factor" that we seek to avoid through smoothing is the data sparsity associated with word-based representations.
the task is to find g such that for every word x, g(x) is much less sparse, but still retains the essential features of x that are useful for predicting its label.