A generative-informed neuro-symbolic framework for syntactic ambiguity resolution: Evidence from Arabic DPs
Syntactic ambiguity poses a persistent challenge for Arabic NLP, particularly in morphologically rich nominal constructions where multiple structu6ral interpretations may be compatible with the same surface sequence. This study proposes a generatively informed neuro-symbolic framework for resolving structural ambiguity in Modern Standard Arabic (MSA) DPs. The framework integrates generative syntactic notions with AraBERT by representing ambiguity as a candidate-based decision task in which linguistically motivated alternatives are explicitly constructed and evaluated through candidate-conditioned input representations. Findings indicate that the model achieved 96.88% accuracy, 95.92% macr...
| Authors | Mohammed Damom, Mohammed Q. Shormani, Muneef Y. Alshawsh et al. |
|---|---|
| Primary category | cs.CL |
| Published | 1 October 2026 |
| Last updated at source | 14 October 2026 |
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