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arXiv Computation and Language By Mohammed Damom, Muneef Y. Alshawsh, Ashraf A. Naji, Mustafa Ali Alhamzi, Fawwaz An-Nashef, Jameel Ahmed Elayah, Mohammed Q. Shormani, Noman AL-Sayadi

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

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The paper introduces a generative‑informed neuro‑symbolic framework that combines generative syntactic theory with AraBERT to resolve structural ambiguity in Modern Standard Arabic noun phrases. By treating ambiguity as a candidate‑based decision task, the model explicitly constructs and evaluates linguistically motivated alternatives, achieving high accuracy (96.88%) and strong F1 scores on an unseen evaluation set. Analysis shows uneven performance across attachment types, with near‑perfect recall for high/VP attachment but lower recall for low/NP/embedded attachment, highlighting challenges in recovering embedded interpretations.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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