Paper: Figures Of Merit For Best-First Probabilistic Chart Parsing

ACL ID W96-0212
Title Figures Of Merit For Best-First Probabilistic Chart Parsing
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 1996
Authors

Best-first parsing methods for natural language try to parse efficiently by considering the most likely constituents first. Some figure of merit is needed by which to compare the likelihood of con- stituents, and the choice of this figure has a sub- stantial impact on the efficiency of the parser. While several parsers described in the literature have used such techniques, there is no published data on their efficacy, much less attempts to judge their relative merits. We propose and evaluate several figures of merit for best-first parsing.