Abstract: A system generates rules for classifying documents are generated by building a vocabulary of features (e.g., words, phrases, acronyms, etc.) that are related to classifying concepts. The system includes a security document reader receives a security document that defines security concepts for a particular project and parses the security document to separate the security concepts. A vocabulary builder receives samples provided by the user that contain information related to the project. For each security concept, the vocabulary builder uses statistical analysis techniques to find features in the samples that are related to that concept. A rule generation assistant, for each security concept, generates rules based on the built vocabulary and the samples. The rule generation assistant uses statistical analysis techniques on the vocabulary and samples to determine features that optimally predict a particular concept. The rules can be used by a downgrader to process information to be distributed.

Abstract: A streaming text data comparator performs real-time text data mining on streaming text data. The comparator receives a streaming text data document and generates a vector representation of the term frequencies relating to an existing document collection. The comparator then transforms the term frequency vector into a projection in a precomputed multidimensional subspace that represents the original document collection. The comparator further calculates a relationship value representing the similarities or differences between the vector representation and the subspace, and compares the relationship value to a predetermined threshold to determine whether the streaming text data document is related to the original document collection. If the streaming text data document is related, the streaming text data comparator intercalates the new document into the document collection. If the new document is not related, the comparator may store or delete the unrelated document.

Abstract: A text summarizer identifies relevant terms in a document, weights the terms and extracts one or more segments to produce a summary or abstract. The various terms in a particular are weighted in relation to an existing document collection. A term weight computer computes term weights for terms in the document, and a threshold comparator compares the term weights to determine if the corresponding terms are relevant to the document collection. Next, a term weight summer adds the term weights for each occurrence of each relevant term in the various segments of the document, and a summation comparator compares the summations to identify a text summarization segment representative of the document. Optionally, relevant terms can be highlighted in the term summarization segment.

Abstract: A text summarizer identifies relevant terms in a document, weights the terms and extracts one or more segments to produce a summary or abstract. The various terms in a particular are weighted in relation to an existing document collection. A term weight computer computes term weights for terms in the document, and a threshold comparator compares the term weights to determine if the corresponding terms are relevant to the document collection. Next, a term weight summer adds the term weights for each occurrence of each relevant term in the various segments of the document, and a summation comparator compares the summations to identify a text summarization segment representative of the document. Optionally, relevant terms can be highlighted in the term summarization segment.

Abstract: A method and system are provided for determining metadata to be assigned to a document. A document is identified to be published to a collection of documents. A category for publishing the document to the collection is automatically recommended using a text classifier. At least one category for the document may be selected, and the selected category is assigned as metadata for the document. A method and system are also provided for constructing a search for a document in a collection of documents. A search query is entered for a document in a collection of documents. A search category for searching for the document in the collection of documents is automatically recommended using a text classifier. At least one search category for searching for the document is selected.

Abstract: Provided are improved methods, apparatus, and computer program products for text differentiation which involves identifying differences between documents with similar content, not merely similar terms, and generating results. Text differentiation provides the ability to find non-similar, or different, content hidden within documents with similar overall content, but not exactly the same content. Text differentiation may be used to quickly identify key differences between similar documents.

Abstract: A method and system are provided for determining metadata to be assigned to a document. A document is identified to be published to a collection of documents. A category for publishing the document to the collection is automatically recommended using a text classifier. At least one category for the document may be selected, and the selected category is assigned as metadata for the document. A method and system are also provided for constructing a search for a document in a collection of documents. A search query is entered for a document in a collection of documents. A search category for searching for the document in the collection of documents is automatically recommended using a text classifier. At least one search category for searching for the document is selected.