Neural network centered language models relieve the sparsity dilemma Incidentally they encode inputs. Word embedding layers make an arbitrary sized vector of every word that comes with semantic associations too. These continuous vectors build the A lot required granularity while in the probability distribution of another phrase.Aerospike raises $11