1. A method of training an information extraction system to extract information from a natural language input, comprising: initializing a structured language model with syntactically annotated training data, the annotated training data including a parse tree for a sentence having syntactic labels comprising a frame label indicating an overall action being referred to by the sentence and slot labels identifying attributes of the action; training the structured language model by generating parses with the initialized structured language model using annotated training data that has semantic constituent labels with semantic constituent boundaries identified, wherein the structured language model is trained as a match constrained parser which generates a set of syntactic parses for a given word string that all match the constituent boundaries specified by the semantic parse, by determining whether unlabeled constituents that define the semantic parse are included in a set of constituents that define the syntactic parse, wherein any parses that do not match the constituent boundaries are discarded; replacing the syntactic labels in the parse tree with joint syntactic and semantic labels based on the generated parses excluding the discarded parses; and retraining the structured language model in which the structured language model generates 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.
1. A method of training an information extraction system to extract information from a natural language input, comprising: initializing a structured language model with syntactically annotated training data, the annotated training data including a parse tree for a sentence having syntactic labels comprising a frame label indicating an overall action being referred to by the sentence and slot labels identifying attributes of the action; training the structured language model by generating parses with the initialized structured language model using annotated training data that has semantic constituent labels with semantic constituent boundaries identified, wherein the structured language model is trained as a match constrained parser which generates a set of syntactic parses for a given word string that all match the constituent boundaries specified by the semantic parse, by determining whether unlabeled constituents that define the semantic parse are included in a set of constituents that define the syntactic parse, wherein any parses that do not match the constituent boundaries are discarded; replacing the syntactic labels in the parse tree with joint syntactic and semantic labels based on the generated parses excluding the discarded parses; and retraining the structured language model in which the structured language model generates 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. 5. The method of claim 1 wherein generating parses comprises: generating syntactic parses with syntactic labels, wherein the syntactic parses are constrained to match the semantic constituent boundaries; and generating semantic parses with semantic labels, wherein the semantic parses are constrained to match the semantic constituent labels in the annotated training data.