Titlebook: Advances in Probabilistic and Other Parsing Technologies; Harry Bunt,Anton Nijholt Book 2000 Springer Science+Business Media Dordrecht 200

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https://doi.org/10.1007/978-3-031-09745-4ties occur in the dependency relations between words. While figuring out the correct dependency, lexical associations play an important role. Our parser uses statistical co-occurrence data to compute lexical associations. We show that sentences can be parsed deterministically by means of global mana

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https://doi.org/10.1007/978-3-319-00366-5cope with ambiguity by using underspecified descriptions of trees. This chapter introduces a novel approach to parsing TAG, namely one that explores how D-theoretic notions may be applied to TAG parsing..Combining the D-theoretic approach to TAG parsing as we do here raises new issues and problems.

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James Palmer,Kenneth Ramsden,Eric Goodgeres in semantic forms. The method is called ‘Semco’ (for semantic co-occurrence analysis) and is specifically targeted at the differential distribution of such co-occurrences in correct and incorrect parses. It uses Bayesian Estimation for the co-occurrence probabilities to achieve higher accuracy fo

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https://doi.org/10.1007/978-1-349-09724-1ciated parsers in which declarative knowledge about language structure and procedural knowledge about language use are equally balanced within a strictly object-oriented specification and implementation framework. In particular, we introduce fundamental message-passing protocols for object-oriented

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