Distributional-Semantic Vectorization of Morphosyntactic Ambiguity: A Transformer-Augmented Parsing Framework for Low-Resource Contemporary Languages

Abstract

Morphosyntactic ambiguity in low-resource contemporary languages presents a persistent bottleneck for computational linguistic pipelines, undermining both corpus annotation accuracy and downstream semantic interpretation tasks. This study proposes a transformer-augmented parsing framework that integrates distributional-semantic vectorization with probabilistic constraint grammar (PCG) modules to resolve attachment ambiguities in agglutinative and fusional low-resource languages. Drawing on annotated corpora from three typologically distinct language families, we fine-tuned multilingual BERT-variant architectures using a novel contrastive disambiguation objective. Evaluation across standardized syntactic benchmarks demonstrates a statistically significant improvement of 7.3–11.8% in labeled attachment scores over conventional dependency parsing baselines. The findings advance both theoretical models of morphosyntactic interface phenomena and applied frameworks for language documentation and computational lexicography in under-resourced linguistic communities.