Abstract: A method for predicting insider threat includes mining electronic data of an organization corresponding to activity of an entity, determining features of the electronic data corresponding to the activity of the entity, classifying the features corresponding to the activity of the entity, determining sequences of classified features matching one or more patterns of insider threat, scoring the entity according to matches of the classified features to the one or more patterns of insider threat, and predicting an insider threat corresponding to the entity according to the score.

Abstract: Detecting propensity profile for a person may comprise receiving artifacts associated with the person; detecting profile characteristics for the person based on the artifacts; receiving a plurality of predefined profiles comprising a plurality of characteristics and relationships between the characteristics over time, each of the plurality of predefined profiles specifying an indication of propensity; matching the profile characteristics for the person with one or more of the plurality of predefined profiles; and outputting one or more propensity indicators based on the matching, the propensity indicators comprising at least an expressed strength of a given propensity in the person at a given time.

Abstract: A hybrid simulation system to model cascading events within a black box system may include a tessellated field operable to allow a plurality of black box components to execute on a processor and communicate without needing a direct connection between the black box components. The black box components have transfer functions defined over a common coordinate system and time.

Abstract: A hybrid simulation system to model cascading events within a black box system may include a tessellated field operable to allow a plurality of black box components to execute on a processor and communicate without needing a direct connection between the black box components. The black box components have transfer functions defined over a common coordinate system and time.

Abstract: Automated question answering is disclosed that relates to the selection of an answer to a question from a pool of potential answers which are manually or automatically extracted from a large collection of textual documents. The a feature extraction component, a feature combination component, an answer selection component, and an answer presentation component, among others, are included. The input to the system is a set of one or more natural language questions and a collection of textual document. The output is a (possibly ranked) set of factual answers to the questions, these answers being extracted from the document collection.

Abstract: A hybrid simulation system to model cascading events within a black box system may include a tessellated field operable to allow a plurality of black box components to execute on a processor and communicate without needing a direct connection between the black box components. The black box components have transfer functions defined over a common coordinate system and time.

Abstract: A hybrid simulation system to model cascading events within a black box system may include a tessellated field operable to allow a plurality of black box components to execute on a processor and communicate without needing a direct connection between the black box components. The black box components have transfer functions defined over a common coordinate system and time.

Abstract: Automated question answering is disclosed that relates to the selection of an answer to a question from a pool of potential answers which are manually or automatically extracted from a large collection of textual documents. The a feature extraction component, a feature combination component, an answer selection component, and an answer presentation component, among others, are included. The input to the system is a set of one or more natural language questions and a collection of textual document The output is a (possibly ranked) set of factual answers to the questions, these answers being extracted from the document collection.

Abstract: Automated question answering is disclosed that relates to the selection of an answer to a question from a pool of potential answers which are manually or automatically extracted from a large collection of textual documents. The a feature extraction component, a feature combination component, an answer selection component, and an answer presentation component, among others, are included. The input to the system is a set of one or more natural language questions and a collection of textual document. The output is a (possibly ranked) set of factual answers to the questions, these answers being extracted from the document collection.

Abstract: A system and method for processing documents by utilizing the textual content and layout of the documents, including visual indicators, to more efficiently and reliably process the documents across various document types. The system and method identifies visually distinguishable elements within the document, such as section and sub-section boundary indicators, to mark, divide and label the boundaries and content type such that the sections are more clearly identifiable and easily processed. The system and method uses known elements, including section heading types, keywords, section type classifiers, sub-section heading constructs, stop words, and the like to adaptively identify and process a broad range of document types. The system and method continually refines and updates these known elements and allows users to discover and define new elements for further refinement and updating.

Abstract: A system and method for processing documents by utilizing the textual content and layout of the documents, including visual indicators, to more efficiently and reliably process the documents across various document types. The system and method identifies visually distinguishable elements within the document, such as section and sub-section boundary indicators, to mark, divide and label the boundaries and content type such that the sections are more clearly identifiable and easily processed. The system and method uses known elements, including section heading types, keywords, section type classifiers, sub-section heading constructs, stop words, and the like to adaptively identify and process a broad range of document types. The system and method continually refines and updates these known elements and allows users to discover and define new elements for further refinement and updating.

Abstract: Automated question answering is disclosed that relates to the selection of an answer to a question from a pool of potential answers which awe manually or automatically extracted from a large collection of textual documents. The a feature extraction component, a feature combination component, an answer selection component, and an answer presentation component, among others, are included. The input to the system is a set of one or more natural language questions and a collection of textual document The output is a (possibly ranked) set of factual answers to the questions, these answers being extracted from the document collection.

Abstract: The present invention is a system, method, and program product that comprises a computer with a collection of documents to be searched. The documents contain free form (natural language) text. We define a set of labels called QA-Tokens, which function as abstractions of phrases or question-types. We define a pattern file, which consists of a number of pattern records, each of which has a question template, an associated question word pattern, and an associated set of QA-Tokens. We describe a query-analysis process which receives a query as input and matches it to one or more of the question templates, where a priority algorithm determines which match is used if there is more than one. The query-analysis process then replaces the associated question word pattern in the matching query with the associated set of QA-Tokens, and possibly some other words. This results in a processed query having some combination of original query tokens, new tokens from the pattern file, and QA-Tokens, possibly with weights.