arXiv cs.CL· Mohammed Damom, Mohammed Q. Shormani, Muneef Y. Alshawsh, Ashraf A. Naji, Mustafa Ali Alhamzi, Fawwaz An-Nashef, Jameel Ahmed Elayah, Noman AL-Sayadi·· 6 天前AI 评分22
生成语法驱动的神经符号框架如何解决阿拉伯语 DP 句法歧义
A generative-informed neuro-symbolic framework for syntactic ambiguity resolution: Evidence from Arabic DPs
AI 导读
一项研究提出生成语法驱动的神经符号框架，结合生成句法理论与 AraBERT，将现代标准阿拉伯语 DP 的结构歧义建模为候选决策任务，在未见评测集上取得 96.88% 准确率、95.92% macro-F1、96.83% weighted F1 和 93.94% binary F1。
正文 · 原文
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:2610.02529v2 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.02529 arXiv-issued DOI via DataCite |
Submission history
From: Mohammed Q. Shormani Mr [view email]
[v1]
Thu, 1 Oct 2026 22:00:08 UTC (890 KB)
[v2]
Fri, 9 Oct 2026 13:13:07 UTC (917 KB)
来源：arXiv cs.CL · arxiv.org