Broad-coverage corpora annotated with semantic role, or argument structure, information are becoming available for the first time. Statistical systems have been trained to automatically label semantic roles from the output of statistical parsers on unannotated text. In this paper, we quantify the effect of parser accuracy on these systems' performance, and examine the question of whether a flatter "chunked" representation of the input can be as effective for the purposes of semantic role identification.

jects that use these technologies for archiving and retrieval. The main goal of the MAESTRO project is to discover, implement, and evaluate various combinations of these technologies to achieve analysis performance that surpasses the sum of the parts. For example, British Prime Minister Tony Blair can be identified in the news by his voice, his appearance, captions, and other cues. A combination of these cues should provide more reliable identification of the Prime Minister than using any of the cues on their own. MAESTRO is a highly multidisciplinary effort, involving contributions from three laboratories across two divisions at SRI. Each of these SRI technologies is described in more detail here. The integrating architecture makes it easy to combine these in different ways, and to incorporate new analysis technologies developed by our team or by others. Multimedia Analysis Technologies on the MAESTRO Score On the MAESTRO Score (see Figure 1), on each line, similar