12,166 research outputs found

    Edge-Based Best-First Chart Parsing

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    Best-first probabilistic chart parsing attempts to parse efficiently by working on edges that are judged 'best' by some probabilistic figure of merit (FOM). Recent work has used proba- bilistic context-free grammars (PCFGs) to sign probabilities to constituents, and to use these probabilities as the starting point for the FOM. This paper extends this approach to us- ing a probabilistic FOM to judge edges (incomplete constituents), thereby giving a much finergrained control over parsing effort. We show how this can be accomplished in a particularly simple way using the common idea of binarizing the PCFG. The results obtained are about a factor of twenty improvement over the best prior results -- that is, our parser achieves equivalent results using one twentieth the number of edges. Furthermore we show that this improvement is obtained with parsing precision and recall levels superior to those achieved by exhaustive parsing

    Message-Passing Protocols for Real-World Parsing -- An Object-Oriented Model and its Preliminary Evaluation

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    We argue for a performance-based design of natural language grammars and their associated parsers in order to meet the constraints imposed by real-world NLP. Our approach incorporates declarative and procedural knowledge about language and language use within an object-oriented specification framework. We discuss several message-passing protocols for parsing and provide reasons for sacrificing completeness of the parse in favor of efficiency based on a preliminary empirical evaluation.Comment: 12 pages, uses epsfig.st

    Learning unification-based grammars using the Spoken English Corpus

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    This paper describes a grammar learning system that combines model-based and data-driven learning within a single framework. Our results from learning grammars using the Spoken English Corpus (SEC) suggest that combined model-based and data-driven learning can produce a more plausible grammar than is the case when using either learning style isolation.Comment: 10 page

    Strong domain variation and treebank-induced LFG resources

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    In this paper we present a number of experiments to test the portability of existing treebank induced LFG resources. We test the LFG parsing resources of Cahill et al. (2004) on the ATIS corpus which represents a considerably different domain to the Penn-II Treebank Wall Street Journal sections, from which the resources were induced. This testing shows an under-performance at both c- and f-structure level as a result of the domain variation. We show that in order to adapt the LFG resources of Cahill et al. (2004) to this new domain, all that is necessary is to retrain the c-structure parser on data from the new domain
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