Abstract: A document similarity detection and classification system is presented. The system employs a case-based method of classifying electronically distributed documents in which content chunks of an unclassified document are compared to the sets of content chunks comprising each of a set of previously classified sample documents in order to determine a highest level of resemblance between an unclassified document and any of a set of previously classified documents. The sample documents have been manually reviewed and annotated to distinguish document classifications and to distinguish significant content chunks from insignificant content chunks. These annotations are used in the similarity comparison process. If a significant resemblance level exceeding a predetermined threshold is detected, the classification of the most significantly resembling sample document is assigned to the unclassified document.

Abstract: A system and method is provided for assisting a human document annotator in recording semantic judgments about the contents of sample electronic documents. A system administrator first configures and stores a document annotation definition at a server computer, providing a precise and consistent structure for annotating documents and portions of documents. Documents intended to serve as sample documents for pattern matching against unknown documents are collected and stored at the server computer. A human annotator located at a client computer connected by a net-work to the server computer requests a display of a sample document to be annotated. A document is transmitted in an annotatable form from the server computer to the client computer. The human document annotator reviews the annotatable document, records semantic judgments about the document using interactive controls displayed with the document, and transmits a set of selected annotation values to the server computer.