Patent ID: 7937397

Claim:
A computer implemented method implemented within a computer system comprising a memory and a CPU for predicting if a model term is contextually related to a given text object, the method comprising: utilizing the memory and the CPU for deriving a context model for the model term; wherein the context model is constructed by training the model term with a collection of documents; wherein the context model comprises a relationship of a set of support features extracted from the collection of documents and multiple sets of weights; wherein each text object in the set of support features corresponds to a weight in a first set of weights, each weight in the first set of weights being a contextual score between the model term and each support feature in the set of support features, each contextual score being based on a contextual correlation between the model term and each support feature in the set of support features, wherein a high correlation would indicate high contextual relevance and a low correlation would indicate low contextual relevance; wherein each support feature in the set of support features corresponds to a weight in a second set of weights, each weight in the second set of weights being a co-occurrence score between the model term and each support feature in the set of support features, each co-occurrence score indicating a frequency of the model term occurring with each support feature in the set of support features; utilizing the memory and the CPU for applying the context model to the given text object; and utilizing the memory and the CPU for obtaining a context model score from the application of the context model to the given text object, wherein the context model score indicates the likelihood of the model term being contextually related to the given text object.