Patent ID: 6925432

Claim:
A method of training a scoring matrix for use by a classification system, the classification system for use in performing classification requests based on natural language text and with use of said scoring matrix which has been based on a set of training data comprising natural language text, the method comprising the steps of: generating an initial scoring matrix comprising a numerical value for each of a set of n classes in association with each of a set of m features, the initial scoring matrix based on said set of training data and, for each element of said set of training data, based on a subset of said features which are comprised in said natural language text of said element of said set of training data and on one of said classes which has been identified therefor; and based on the initial scoring matrix and said set of training data, generating a discriminatively trained scoring matrix for use by said classification system by adjusting one or more of said numerical values such that a greater degree of discrimination exists between competing ones of said classes when said classification requests are performed, thereby resulting in a reduced classification error rate, wherein said step of adjusting said numerical values is performed with use of a Generalized Probabilistic Descent algorithm and wherein said step of adjusting said numerical values comprises, for each element of said set of training data, modifying values associated with the identified class and values associated with one or more of the other classes such that a score obtained for said element of said set of training data based on said modified values associated with the identified class is improved relative to one or more scores obtained for said element of said set of training data based on said modified values associated with said one or more other classes.