1. A method comprising: developing, via a processor, a statistical model for a natural language understanding application using human knowledge exclusive of any annotated data; during a first execution of the natural language understanding application via the processor: receiving a sequence of words to yield a received sequence of words; assigning a sequence of tags to the received sequence of words by using the statistical model, to yield annotated words; and when sufficient annotated words become available, updating the statistical model; developing a replacement statistical model for the natural language understanding application using the annotated words by: developing a first part of the replacement model without the human knowledge; developing a second part of the replacement model from both the human knowledge and the annotated words; and when the received and subsequently annotated data is sufficient, weighting a contribution from the first part more to the assigning than when the received and subsequently annotated data is insufficient; and during a second execution of the natural language understanding application, receiving a second sequence of words and assigning a second sequence of tags to the second sequence of words by using the replacement statistical model.