Probabilistic Valency Frame Decomposition in Agglutinative Languages: A Cross-Corpus Computational Architecture for Morphosyntactic Dependency Mapping

Abstract

This study addresses persistent limitations in dependency grammar annotation when applied to morphologically rich agglutinative languages, where traditional valency-frame models exhibit systematic degradation in predictive accuracy. Drawing on aligned treebank corpora from Turkish, Finnish, and Kazakh, we introduce a probabilistic decomposition architecture that integrates hierarchical morpheme-boundary parsing with cross-lingual valency slot alignment. The proposed framework employs conditional random field layering atop transformer-encoded lemma sequences, enabling dynamic recalibration of argument structure under syntactic ambiguity. Experimental evaluation across three held-out corpora demonstrates a mean labeled attachment score improvement of 6.3 percentage points over established baselines. These results suggest that morphosyntactic dependency mapping can be substantially enhanced through language-family-sensitive probabilistic modeling, with direct implications for low-resource NLP pipeline construction and typological grammar research.