Measuring Efficiency in High-accuracy, Broad-coverage Statistical Parsing
Very little attention has been paid to the comparison of efficiency between high accuracy statistical parsers. This paper proposes one machine-independent metric that is general enough to allow comparisons across very different parsing architectures. This metric, which we call "events considered", measures the number of "events", however they are defined for a particular parser, for which a probability must be calculated, in order to find the parse. It is applicable to single-pass or multi-stage parsers. We discuss the advantages of the metric, and demonstrate its usefulness by using it to compare two parsers which differ in several fundamental ways.