Tech Report CS-96-12
Figures of Merit for Best-First Probabilistic Chart Parsing
Sharon A. Caraballo and Eugene Charniak
April 1996
Abstract:
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 constituents, and the choice of this figure has a substantial 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.(complete text in pdf or gzipped postscript)
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