Abstract: Systems, methods, and apparatus for clustering resources using rare features are provided. For example, an environment includes an extraction module, an index module, and a cluster module. The extractions module identifies a set of resources and extracts a plurality of features from the resources. The plurality of features may be rare features. The index module identifies and generates a rare features index. The cluster module identifies at least two resources that share rare features, creates one or more clusters based on the identified at least two resources, and associates resources that share similar features with the one or more clusters. Resources that do not share similar features are not associated with the one or more clusters. Identifying at least two resources that share rare features is based at least upon a threshold.

Abstract: Systems, methods, and apparatus for clustering resources using rare features are provided. For example, an environment includes an extraction module, an index module, and a cluster module. The extractions module identifies a set of resources and extracts a plurality of features from the resources. The plurality of features may be rare features. The index module identifies and generates a rare features index. The cluster module identifies at least two resources that share rare features, creates one or more clusters based on the identified at least two resources, and associates resources that share similar features with the one or more clusters. Resources that do not share similar features are not associated with the one or more clusters. Identifying at least two resources that share rare features is based at least upon a threshold.

Abstract: Systems, methods, and apparatus for clustering resources using rare features are provided. For example, an environment includes an extraction module, an index module, and a cluster module. The extractions module identifies a set of resources and extracts a plurality of features from the resources. The plurality of features may be rare features. The index module identifies and generates a rare features index. The cluster module identifies at least two resources that share rare features, creates one or more clusters based on the identified at least two resources, and associates resources that share similar features with the one or more clusters. Resources that do not share similar features are not associated with the one or more clusters. Identifying at least two resources that share rare features is based at least upon a threshold.

Abstract: Systems, methods, and apparatus for clustering resources using rare features are provided. For example, an environment includes an extraction module, an index module, and a cluster module. The extractions module identifies a set of resources and extracts a plurality of features from the resources. The plurality of features may be rare features. The index module identifies and generates a rare features index. The cluster module identifies at least two resources that share rare features, creates one or more clusters based on the identified at least two resources, and associates resources that share similar features with the one or more clusters. Resources that do not share similar features are not associated with the one or more clusters. Identifying at least two resources that share rare features is based at least upon a threshold.

Abstract: A search system and method are provided that uses taxonomies, entities, facets, and ontologies to provide a user with a more comprehensive set of search results in response to a query. The search system has an indexing engine that performs one or more indexing steps that permit the search engine to return a comprehensive set of search results. For example, the indexing engine may index a document according to a set of synsets so that the search engine may use the synsets, during retrieval of results to a query, to return a more comprehensive set of search results.

Abstract: A terminological system automatically generates sub-categories from categories of a knowledge base. The knowledge base includes a plurality of hierarchically arranged categories, as well as terms associated with the categories. A subset of the categories of the knowledge base are designated “dimensional categories.” The system also stores a corpus of documents, including themes and corresponding theme weights for each document. A target category is selected to generate sub-categories. A set of themes from the corpus of documents are selected for each term. Dimensional category vectors, one for each term, are generated by associating the set of themes for a term to a dimensional category in the knowledge base. The dimensional category vectors for each term are analyzed to determine if one or more clusters of terminological groups exist to generate new sub-categories. A content processing system, which generates themes and theme weights, is also disclosed.