9. An information extraction system comprising: a semantic schema comprising a multilevel template with a root level and a leaf level, the root level having a frame label identifying an action to be performed, the leaf level having slots identifying attributes of the action; and a computer processor that is a component of a computing device that receives a natural language input and generates a candidate parse by parsing the natural language input with a structured language model that generates hypothesis parses of a portion of the natural language input by applying the multilevel template and accepting only those hypothesis parses that completely match the structure of the multilevel template, wherein the structured language model is trained by generating a set of syntactic parses for a given word string that all match semantic constituent boundaries, by determining whether unlabeled constituents that define a semantic parse is included in a set of constituents that define the syntactic parse, wherein parses that do not match the semantic constituent boundaries are discarded, and replacing syntactic labels in a parse tree with joint syntactic and semantic labels based on the generated parses excluding the discarded parses, and retraining the structured language model by generating parses that are constrained to identically match the semantic constituent labels of the joint syntactic and semantic labels and constrained to match all of the semantic constituent boundaries.
9. An information extraction system comprising: a semantic schema comprising a multilevel template with a root level and a leaf level, the root level having a frame label identifying an action to be performed, the leaf level having slots identifying attributes of the action; and a computer processor that is a component of a computing device that receives a natural language input and generates a candidate parse by parsing the natural language input with a structured language model that generates hypothesis parses of a portion of the natural language input by applying the multilevel template and accepting only those hypothesis parses that completely match the structure of the multilevel template, wherein the structured language model is trained by generating a set of syntactic parses for a given word string that all match semantic constituent boundaries, by determining whether unlabeled constituents that define a semantic parse is included in a set of constituents that define the syntactic parse, wherein parses that do not match the semantic constituent boundaries are discarded, and replacing syntactic labels in a parse tree with joint syntactic and semantic labels based on the generated parses excluding the discarded parses, and retraining the structured language model by generating parses that are constrained to identically match the semantic constituent labels of the joint syntactic and semantic labels and constrained to match all of the semantic constituent boundaries. 13. The system of claim 9 , wherein each node of annotated training data is associated with a word from a natural language input, and wherein each node is also associated with a k-prefix that identifies previous words in the natural language input.