9. A computer-assisted method for recognizing and labeling instances of name classes in textual environments, comprising the following steps: A. receiving a marked string of words and using said marked string to estimate a plurality of HMM parameters, and using said plurality of HMM parameters to generate an HMM having N states, each of said N states representing a name class and having a plurality of member states; B. receiving an unmarked string of K words, K being an integer; C. recognizing and labeling a plurality of name class instances in said unmarked string of K words, including the following substeps: i. receiving a k.sup.th and a (k+1).sup.th word from said unmarked string of K words; ii. determining, from said HMM parameters, a transition probability representing a likelihood of said (k+1).sup.th word being a member of an i.sup.th state, given said k.sup.th word being a member of a j.sup.th state, and given said (k+1).sup.th word immediately following said k.sup.th word, said i.sup.th and k.sup.th states being member states of said N states; iii. repeating step (ii) for all i from 1 to N and for all j from 1 to N, whereby N.sup.2 transition probabilities are determined, and retaining the highest transition probability to the (k+1).sup.th word in each of the N states; iv. repeating steps (i), (ii) and (iii) for all k from 1 to K, whereby N.multidot.(K-1) transition probabilities are retained; v. identifying a highest probability path from the K.sup.th word back to the first word; and, vi. labeling each of said K words with said name class state through which said highest probability path from the N identified paths passes.
9. A computer-assisted method for recognizing and labeling instances of name classes in textual environments, comprising the following steps: A. receiving a marked string of words and using said marked string to estimate a plurality of HMM parameters, and using said plurality of HMM parameters to generate an HMM having N states, each of said N states representing a name class and having a plurality of member states; B. receiving an unmarked string of K words, K being an integer; C. recognizing and labeling a plurality of name class instances in said unmarked string of K words, including the following substeps: i. receiving a k.sup.th and a (k+1).sup.th word from said unmarked string of K words; ii. determining, from said HMM parameters, a transition probability representing a likelihood of said (k+1).sup.th word being a member of an i.sup.th state, given said k.sup.th word being a member of a j.sup.th state, and given said (k+1).sup.th word immediately following said k.sup.th word, said i.sup.th and k.sup.th states being member states of said N states; iii. repeating step (ii) for all i from 1 to N and for all j from 1 to N, whereby N.sup.2 transition probabilities are determined, and retaining the highest transition probability to the (k+1).sup.th word in each of the N states; iv. repeating steps (i), (ii) and (iii) for all k from 1 to K, whereby N.multidot.(K-1) transition probabilities are retained; v. identifying a highest probability path from the K.sup.th word back to the first word; and, vi. labeling each of said K words with said name class state through which said highest probability path from the N identified paths passes. 11. A method according to claim 9, wherein some of said transitional probabilities are conditioned upon said name class states, some of said transitional probabilities are conditioned upon said member states, and some of said transitional probabilities are conditioned upon both name class states and said member states.