Optimized Distributional-Semantic Parsing via Cross-Lingual Transfer Alignment: A Constrained Latent-Variable Framework for Low-Resource Morphosyntactic Disambiguation

Abstract

Morphosyntactic disambiguation in low-resource language environments remains a critical bottleneck in contemporary natural language processing and corpus-driven linguistic analysis. This study introduces a constrained latent-variable framework integrating distributional-semantic parsing with cross-lingual transfer alignment, specifically engineered to address token-level ambiguity in agglutinative and fusional low-resource languages. Leveraging multilingual contextual embeddings calibrated through iterative expectation-maximization over annotated treebank corpora, the proposed architecture achieves statistically significant improvements in part-of-speech tagging accuracy (F1 = 0.923) and dependency arc labeling (UAS = 87.4%) over competitive baseline systems. Ablation studies confirm the discriminative contribution of constrained latent priors in resolving structural ambiguity. Findings advance the methodological foundation for scalable, cross-linguistically portable disambiguation pipelines applicable across typologically divergent language families.