1. A natural language understanding system, comprising: a decoder; a conditional random field model accessible by the decoder, the conditional random field model using a computer to assign a conditional probability of a state sequence given an observed vector of features to statistically model alignments between textual characters in an observed natural language input and semantic frames of a semantic structure, the semantic frames corresponding to states in the state sequence, wherein the conditional random field model models prior knowledge of relationships between elements of the semantic frames by assigning the conditional probability based on whether the observed vector of features includes command prior features, the command prior features being independent of the observed natural language input and indicative of a prior likelihood of commands defined by the semantic frames; and a computer processing unit, being a functional hardware component of the system and activated by the decoder and conditional model, facilitating modeling alignments.