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.
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Cite this article
APA 7
Damom, M., Alshawsh, M. Y., Naji, A. A., Alhamzi, M. A., An-Nashef, F., Elayah, J. A., Shormani, M. Q., & AL-Sayadi, N. (2026). A generative-informed neuro-symbolic framework for syntactic ambiguity resolution: Evidence from Arabic DPs. https://omanscience.com/en/articles/a-generative-informed-neuro-symbolic-framework-for-syntactic-ambiguity-resolution-evidence-from-arabic-dps
MLA 9
Damom, Mohammed, et al. "A generative-informed neuro-symbolic framework for syntactic ambiguity resolution: Evidence from Arabic DPs." https://omanscience.com/en/articles/a-generative-informed-neuro-symbolic-framework-for-syntactic-ambiguity-resolution-evidence-from-arabic-dps.
Chicago (author–date)
Damom, Mohammed, Muneef Y. Alshawsh, Ashraf A. Naji, Mustafa Ali Alhamzi, Fawwaz An-Nashef, Jameel Ahmed Elayah, Mohammed Q. Shormani, and Noman AL-Sayadi. 2026. "A generative-informed neuro-symbolic framework for syntactic ambiguity resolution: Evidence from Arabic DPs." https://omanscience.com/en/articles/a-generative-informed-neuro-symbolic-framework-for-syntactic-ambiguity-resolution-evidence-from-arabic-dps.
Harvard
Damom, M., Alshawsh, M. Y., Naji, A. A., Alhamzi, M. A., An-Nashef, F., Elayah, J. A., Shormani, M. Q. and AL-Sayadi, N. (2026) 'A generative-informed neuro-symbolic framework for syntactic ambiguity resolution: Evidence from Arabic DPs', Available at: https://omanscience.com/en/articles/a-generative-informed-neuro-symbolic-framework-for-syntactic-ambiguity-resolution-evidence-from-arabic-dps.
Vancouver
Damom M, Alshawsh MY, Naji AA, Alhamzi MA, An-Nashef F, Elayah JA, et al. A generative-informed neuro-symbolic framework for syntactic ambiguity resolution: Evidence from Arabic DPs. https://omanscience.com/en/articles/a-generative-informed-neuro-symbolic-framework-for-syntactic-ambiguity-resolution-evidence-from-arabic-dps
IEEE
M. Damom, M. Y. Alshawsh, A. A. Naji, M. A. Alhamzi, F. An-Nashef, J. A. Elayah, M. Q. Shormani, and N. AL-Sayadi, "A generative-informed neuro-symbolic framework for syntactic ambiguity resolution: Evidence from Arabic DPs," https://omanscience.com/en/articles/a-generative-informed-neuro-symbolic-framework-for-syntactic-ambiguity-resolution-evidence-from-arabic-dps.