6. A named-entity recognition system comprising: a multilevel hidden Markov model, said hidden Markov model including: (a) at least one upper level including a first plurality of states of a predefined classification of named entities, and (b) at least one lower level including a second plurality of states of a predefined classification of words, wherein at least one of the transitions between the upper states is conditioned on the output of the previous lower states, and at least one of the transitions between the lower state is conditioned on the output of the previous upper state.
6. A named-entity recognition system comprising: a multilevel hidden Markov model, said hidden Markov model including: (a) at least one upper level including a first plurality of states of a predefined classification of named entities, and (b) at least one lower level including a second plurality of states of a predefined classification of words, wherein at least one of the transitions between the upper states is conditioned on the output of the previous lower states, and at least one of the transitions between the lower state is conditioned on the output of the previous upper state. 7. A named-entity recognition system according to claim 6, wherein said Markov model includes a first language model for assigning transition probabilities between the states of the upper level, and includes a second language model for assigning transition probabilities between the words of the lower level.