ALGORITHMS THAT LEARN TO EXTRACT INFORMATION m

ABSTRACT All of BBN's research under the TIPSTER III program,has focused,on,doing extraction by applying statistical models trained on annotated data, rather than by using programs that execute hand-written rules. Within the context of MUC- 7, the SIFT system for extraction of template entities (TE) and template relations (TR) used a novel, integrated syntactic/semantic language model to extract sentence level information, and then synthesized information across sentences using in part a trained model for cross-sentence relations. At the named entity (NE) level as well, in both MET-1 and MUC-7, BBN employed a trained, HMM-based model. The results in these TIPSTER evaluations are evidence that such trained systems, even at their current level of development, can perform roughly on a par with those based on rules handtailored by experts. In addition, such trained systems have some significant advantages: •,They can be easily ported to new domains