On the Distinction between Model-Theoretic and Generative-Enumerative Syntactic Frameworks
LACL '01 Proceedings of the 4th International Conference on Logical Aspects of Computational Linguistics
Eliminative parsing with graded constraints
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Acceptability prediction by means of grammaticality quantification
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Gradience, constructions and constraint systems
CSLP'04 Proceedings of the First international conference on Constraint Solving and Language Processing
Grammar error detection with best approximated parse
IWPT '09 Proceedings of the 11th International Conference on Parsing Technologies
Model-theory of property grammars with features
IWPT '11 Proceedings of the 12th International Conference on Parsing Technologies
Property grammar parsing seen as a constraint optimization problem
FG'10/FG'11 Proceedings of the 15th and 16th international conference on Formal Grammar
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Although the observation of grammaticality judgements is well acknowledged, their formal representation faces problems of different kinds: linguistic, psycholinguistic, logical, computational. In this paper we focus on addressing some of the logical and computational aspects, relegating the linguistic and psycholinguistic ones in the parameter space. We introduce a model-theoretic interpretation of Property Grammars, which lets us formulate numerical accounts of grammaticality judgements. Such a representation allows for both clear-cut binary judgements, and graded judgements. We discriminate between problems of Intersective Gradience (i.e., concerned with choosing the syntactic category of a model among a set of candidates) and problems of Subsective Gradience (i.e., concerned with estimating the degree of grammatical acceptability of a model). Intersective Gradience is addressed as an optimisation problem, while Subsective Gradience is addressed as an approximation problem.