The Smart/Empire TIPSTER IR System

We attack each task through a combination of statistical and linguistic approaches. The proposed statistical approaches extend existing methods in IR by performing statistical computations within the context of another query or document. The proposed linguistic approaches build on existing work in information extraction and rely on a new technique for trainable partial parsing. In short, our integrated approach uses both statistical and linguistic sources to identify selected relationships among important terms in a query or text. The relationships are encoded as TIPSTER annotations [7]. We then use the extracted relationships: (1) to discard or reorder retrieved texts (for high-precision text retrieval); (2) to locate redundant information (for near-duplicate document detection); and (3) to generate coherent synopses (for context-dependent text summarization).