[Submitted on 1 Oct 2026]
Title:A generative-informed neuro-symbolic framework for syntactic ambiguity resolution: Evidence from Arabic DPs
View a PDF of the paper titled A generative-informed neuro-symbolic framework for syntactic ambiguity resolution: Evidence from Arabic DPs, by Mohammed Damom and 6 other authors
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Abstract: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% macro-F1, 96.83% weighted F1, and 93.94% binary F1 on the unseen evaluation set. Class-level analysis revealed asymmetric performance, with recall of 99.71% for High/VP Attachment (N1) and 89.26% for Low/NP/Embedded Attachment (N2), indicating greater difficulty in recovering the embedded interpretation. The study concludes that formal syntactic representations can be operationalized within Transformer-based NLP as an explicit interface between linguistic structure and contextual neural modeling, providing a controlled and interpretable approach to Arabic syntactic ambiguity resolution and beyond.
Subjects:
Computation and Language (cs.CL)
ACM classes: F.2.2; I.2.7
Report number: 28 pages, 3 Figures, 2 Tables
Cite as: arXiv:2610.02529 [cs.CL]
(or arXiv:2610.02529v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2610.02529
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Mohammed Q. Shormani Mr [view email] [v1] Thu, 1 Oct 2026 22:00:08 UTC (890 KB)
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