[
    {
        "id": "osp-22454",
        "type": "article-journal",
        "title": "A generative-informed neuro-symbolic framework for syntactic ambiguity resolution: Evidence from Arabic DPs",
        "author": [
            {
                "family": "Damom",
                "given": "Mohammed"
            },
            {
                "family": "Alshawsh",
                "given": "Muneef Y."
            },
            {
                "family": "Naji",
                "given": "Ashraf A."
            },
            {
                "family": "Alhamzi",
                "given": "Mustafa Ali"
            },
            {
                "family": "An-Nashef",
                "given": "Fawwaz"
            },
            {
                "family": "Elayah",
                "given": "Jameel Ahmed"
            },
            {
                "family": "Shormani",
                "given": "Mohammed Q."
            },
            {
                "family": "AL-Sayadi",
                "given": "Noman"
            }
        ],
        "URL": "https://omanscience.com/en/articles/a-generative-informed-neuro-symbolic-framework-for-syntactic-ambiguity-resolution-evidence-from-arabic-dps",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "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."
    }
]