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. 13. A method according to claim 9, wherein each of said member states is representative of a single word.