Learning Computational Grammars

This paper reports on the "Learning Computational Grammars" (LCG) project, a postdoc network devoted to studying the application of machine learning techniques to grammars suitable for computational use. We were interested in a more systematic survey to understand the relevance of many factors to the success of learning, esp. the availability of annotated data, the kind of dependencies in the data, and the availability of knowledge bases (grammars). We focused on syntax, esp. noun phrase (NP) syntax.

Multi-Syllable Phonotactic Modelling

This paper describes a novel approach to constructing phonotactic models. The underlying theoretical approach to phonological description is the multisyllable approach in which multiple syllable classes are defined that reflect phonotactically idiosyncratic syllable subcategories. A new finite-state formalism, OFS Modelling, is used as a tool for encoding, automatically constructing and generalising phonotactic descriptions. Language-independent prototype models are constructed which are instantiated on the basis of data sets of phonological strings, and generalised with a clustering algorithm. The resulting approach enables the automatic construction of phonotactic models that encode arbitrarily close approximations of a language's set of attested phonological forms. The approach is applied to the construction of multi-syllable word-level phonotactic models for German, English and Dutch.