Abstract: In one embodiment, a device in a network classifies Internet content data using one or more classifiers to identify a plurality of content classes for the content data. Each content class has a corresponding classification score based on the classification. The device determines whether any of the classification scores exceed a threshold level. The device identifies a set of content groups, where each of the plurality of content classes is associated with one of the content groups. The device associates the content data with a selected one of the content groups based on a determination that the classification scores for the plurality of content classes do not exceed the threshold level.

Abstract: In one embodiment, a device in a network classifies Internet content data using one or more classifiers to identify a plurality of content classes for the content data. Each content class has a corresponding classification score based on the classification. The device determines whether any of the classification scores exceed a threshold level. The device identifies a set of content groups, where each of the plurality of content classes is associated with one of the content groups. The device associates the content data with a selected one of the content groups based on a determination that the classification scores for the plurality of content classes do not exceed the threshold level.

Abstract: According to one aspect, a method of assessing typicality of a first name that includes a plurality of characters includes obtaining the first name, determining at least a first N-gram size, and extracting a first plurality of N-grams of the first N-gram size from the first name. The first plurality of N-grams is analyzed with respect to a model. Analyzing the first plurality of N-grams with respect to the model includes obtaining a first score. Finally, the method includes determining whether the first score indicates that the first name is typical.

Abstract: According to one aspect, a method of assessing typicality of a first name that includes a plurality of characters includes obtaining the first name, determining at least a first N-gram size, and extracting a first plurality of N-grams of the first N-gram size from the first name. The first plurality of N-grams is analyzed with respect to a model. Analyzing the first plurality of N-grams with respect to the model includes obtaining a first score. Finally, the method includes determining whether the first score indicates that the first name is typical.

Abstract: Meta-content analysis and annotation upon the body of email documents, and other electronic documents, and to create a displayable index of these instances of meta-content, which is sorted and annotated by type are provided. In addition, the electronic document is enhanced by providing links for the semantic foci to external documents containing related information.