A Transformer-Enhanced Deep Learning Framework for Contextual Morphological Parsing in NLP Applications

Morphological parsing—the segmentation and labeling of the minimal meaningful units within words remains a core challenge for natural language processing, particularly in morphologically rich and low-resource languages. We introduce a Transformer-enhanced deep learning framework that jointly performs morpheme boundary detection and label classification in a single end-to-end model. Our approach fuses character-level embeddings with subword contextualization, employs sinusoidal positional encodings, and augments multi-head self-attention to emphasize affix sequences. A novel joint decoding mechanism synchronizes segmentation and labeling, reducing error propagation inherent in pipelined architectures. We evaluate the proposed model on multiple languages with diverse typological characteristics, achieving an average segmentation F1 improvement of 3.7 % and a labeling accuracy gain of 4.2 % over state-of-the-art CNN-BiLSTM and subword tagging baselines. Embedding visualizations reveal clear semantic and morphological clusters, confirming effective context-driven representation learning. Additionally, an interactive 3D embedding interface facilitates qualitative error analysis and lexicon refinement in low-resource settings. Our framework demonstrates scalability and adaptability, outperforming existing systems in both high- and low-resource scenarios while maintaining robustness on unseen morphemes. Future work will address dynamic input handling for extremely long word forms and explore model compression techniques for real-time deployment. This study establishes a unified, scalable solution for high-precision morphological analysis across typologically varied languages.