Computational nonlinear morphology: with emphasis on semitic languages
Computational nonlinear morphology: with emphasis on semitic languages
Active learning for part-of-speech tagging: accelerating corpus annotation
LAW '07 Proceedings of the Linguistic Annotation Workshop
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This paper describes a model that has been developed in the Turgama Project at Leiden University to meet the challenges encountered in the computational analysis of ancient Syriac Biblical manuscripts. The small size of the corpus, the absence of native speakers, and the variation attested in the multitude of textual witnesses require a model of encoding---rather than tagging---that moves from the formal distributional registration of linguistic elements to functional deductions. The model is illuminated by an example from verb inflection. It shows how a corpus-based analysis can improve upon the inflectional paradigms given in traditional grammars and how the various orthographic representations can be accounted for by an encoding system that registers both the paradigmatic forms and their attested realizations.