9. A method for generating a natural language understanding model, comprising: collecting a plurality of unlabeled utterances; generating via a processor a plurality of call types for a natural language understanding model based on call types with high probabilities in an existing natural language understanding model for a different application, each of the plurality of call types having utterances selected from the plurality of unlabeled utterances, the utterances used to generate the plurality of call types representing a subset of the collection of utterances; and generating the natural language understanding model using call type information contained within the subset of utterances, wherein the natural language understanding model is generated prior to receipt of manually labeled utterance data.
9. A method for generating a natural language understanding model, comprising: collecting a plurality of unlabeled utterances; generating via a processor a plurality of call types for a natural language understanding model based on call types with high probabilities in an existing natural language understanding model for a different application, each of the plurality of call types having utterances selected from the plurality of unlabeled utterances, the utterances used to generate the plurality of call types representing a subset of the collection of utterances; and generating the natural language understanding model using call type information contained within the subset of utterances, wherein the natural language understanding model is generated prior to receipt of manually labeled utterance data. 10. The method of claim 9 , wherein the manually labeled utterance data is generated using an annotation guide that is created using a portion of the subset of utterances.