Abstract: An automatic, system-generated, multi-faceted comprehension and response capability, using Natural Language Processing, to provide value specific answers from available unstructured data, documents and text. Questions and queries are interpreted by the system's capability to determine the type of questions and provide a response or answer based on the data or information available. If the answer is in the ingested data, a response is provided that is either; a list of documents, a list of document snippets with the answer contained in the snippets, a formalized and templated response, or a highly relevant hand curated response.

Abstract: Dynamic categorization of documents from a semi-static classification taxonomy through the use of key terms, concepts, and entities. Dynamic categorization is a method for retrieving documents that are relevant to a specific category, which can be defined at the time the documents are needed. This is in contrast to a priori sorting and tagging (identifying) documents as to what categories they belong. The categories can be defined not just as a set of key words but may also include phrases, entities and/or relationships found in the document(s), complex field queries, weighted queries against words, as well as exclusion conditions.

Abstract: Various data or document processing systems may benefit from an improved machine learning process for information extraction. For example, certain data or document processing systems may benefit from enhanced Semantic Vector Rules and a lexical knowledge base used to extract information from the text. A method may include analyzing a set of documents including a plurality of text. The method may also include extracting information from the plurality of text based on one or more semantic vector rules. In addition, the method may include updating the one or more semantic vector rules to include at least one new semantic vector rule based on a semantic rule state evaluation.

Abstract: Various data or document processing systems may benefit from an improved machine learning process for information extraction. For example, certain data or document processing systems may benefit from enhanced Semantic Vector Rules and a lexical knowledge base used to extract information from the text. A method may include analyzing a set of documents including a plurality of text. The method may also include extracting information from the plurality of text based on a lexicon. In addition, the method may include updating the lexicon with at least one new term based on one or more semantic vector rules.