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A generative-informed neuro-symbolic framework for syntactic ambiguity resolution: Evidence from Arabic DPs

arXiv · · Significant research

NLP Arabic AI Research LLM

Summary

This study addresses the persistent challenge of syntactic ambiguity in Modern Standard Arabic (MSA) DPs, particularly in morphologically rich nominal constructions. It proposes a generative-informed neuro-symbolic framework that integrates generative syntactic notions with AraBERT to resolve structural ambiguity. The framework achieved 96.88% accuracy and 95.92% macro-F1 on an unseen evaluation set by treating ambiguity as a candidate-based decision task. Why it matters: This approach provides a controlled and interpretable method for Arabic syntactic ambiguity resolution, demonstrating how formal linguistic representations can enhance Transformer-based NLP for complex languages.

Keywords

Arabic NLP · Syntactic ambiguity · Neuro-symbolic · AraBERT · Generative syntax

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