Learnable classes of categorial grammars
Learnable classes of categorial grammars
Specifying syntactic structures
On the logic and learning of language
On the logic and learning of language
GraSp: grammar learning from unlabelled speech corpora
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
Rough Sets and Learning by Unification
Fundamenta Informaticae - New Frontiers in Scientific Discovery - Commemorating the Life and Work of Zdzislaw Pawlak
Rough Sets and Learning by Unification
Fundamenta Informaticae - New Frontiers in Scientific Discovery - Commemorating the Life and Work of Zdzislaw Pawlak
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In categorial systems with a fixed structural component, the learning problem comes down to finding the solution for a set of type-assignment equations. A hard-wired structural component is problematic if one want to address issues of structural variation. Our starting point is a type-logical architecture with separate modules for the logical and the structural components of the computational system. The logical component expresses invariants of grammatical composition; the structural component captures variation in the realization of the correspondence between form and meaning. Learning in this setting involves finding the solution to both the type-assignment equations and the structural equations of the language at hand. We develop a view on these two subtasks which pictures learning as a process moving through a two-stage cycle. In the first phase of the cycle, type assignments are computed statically from structures. In the second phase, the lexicon is enhanced with facilities for structural reasoning. These make it possible to dynamically relate structures during on-line computation, or to establish off-line lexical generalizations. We report on the initial experiments in [15] to apply this method in the context of the Spoken Dutch Corpus.For the general type-logical background, we refer to [12]; 搂1 has a brief recap of some key features.