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Neural network dependent language models relieve the sparsity difficulty Incidentally they encode inputs. Term embedding levels develop an arbitrary sized vector of every term that incorporates semantic associations too. These continual vectors build the much needed granularity within the probability distribution of the next word.This is the most c