New figures of merit for best-first probabilistic chart parsing

Authors:
Sharon A. Caraballo;Eugene Charniak
Affiliations:
Brown University;Brown University
Venue:
Computational Linguistics
Year:
1998

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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 little 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, and we identify an easily computable figure of merit that provides excellent performance on various measures and two different grammars.