초록 ▼

A computer assisted method for recognizing and labeling instances of name classes in textual environments is described. The invention receives training text having instances of named-entity classes labeled, from which it generates a database of bigram and unigram occurrences. The invention uses the

A computer assisted method for recognizing and labeling instances of name classes in textual environments is described. The invention receives training text having instances of named-entity classes labeled, from which it generates a database of bigram and unigram occurrences. The invention uses the database of bigram and unigram occurrences to form a two level Hidden Markov Model with single output states at the lower level. The invention also receives a series of input text to be processed and labeled with respect to the name classes, and the invention uses the two level Hidden Markov Model to recognize and label instances of named-entity classes in the input text.

대표청구항 ▼

[ What is claimed is:] [16.] A computer system for recognizing and labeling instances of name classes in textual environments, comprising:A. means for providing a marked string of words and using said marked string to estimate a plurality of HMM parameters, and using said plurality of HMM parameters

[ What is claimed is:] [16.] A computer system for recognizing and labeling instances of name classes in textual environments, comprising:A. means for providing 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. means for receiving an unmarked string of K words, K being an integer;C. means for recognizing and labeling a plurality of name class instances in said unmarked string of K words, including:i. means for receiving a k.sup.th and a (k+1).sup.th word from said unmarked string of K words;ii. means for 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. means for 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. means for repeating steps (i), (ii) and (iii) for all k from 1 to K, whereby N.multidot.(K-1) transition probabilities are retained;v. means for identifying a highest probability path from the K.sup.th word back to the first word; and,vi. means for labeling each of said K words with said name class state through which said highest probability path from the N identified paths passes.