Summary
Figuring out the exact structure of sentences in Arabic can be tricky because the same words can fit multiple meanings. The authors created a new computer method that combines traditional grammar ideas with a powerful language AI called AraBERT to decide between different possible sentence structures. Their approach builds clear options and lets the AI choose the best one, making it easier to understand complex Arabic phrases. The method worked well, correctly interpreting most cases, though some tricky sentence forms were harder to get right. This shows that mixing grammar rules with AI can help computers better understand Arabic language ambiguities.
What this means in practice
- •For arabic nlp developers: Improve Arabic language processing tools by explicitly modeling possible sentence structures to resolve ambiguous noun phrases in text.
- •For machine translation teams: Enhance accuracy in Arabic to other language translations by reducing syntactic misunderstandings in rich nominal phrases.
Authors
Mohammed Damom, Muneef Y. Alshawsh, Ashraf A. Naji, Mustafa Ali Alhamzi, Fawwaz An-Nashef, Jameel Ahmed Elayah, Mohammed Q. Shormani, Noman AL-Sayadi
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.