发表机构
Hajjah University; Ibb University; Sana'a University（哈杰大学; 伊卜大学; 萨那大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对阿拉伯语名词短语的结构歧义，提出融合生成句法理论与AraBERT的神经符号框架，通过候选决策任务消解歧义，在未见集上取得96.88%准确率，验证了形式句法可操作化为Transformer接口的有效性。
AI 中文摘要
句法歧义对阿拉伯语自然语言处理（NLP）构成了持续挑战，尤其是在形态丰富的名词性结构中，多种结构解释可能与同一表层序列兼容。本研究提出一个生成式信息增强的神经符号框架，用于解决现代标准阿拉伯语（MSA）中DP（名词短语）的结构歧义。该框架将生成句法概念与AraBERT相结合，将歧义表示为基于候选的决策任务，其中语言上合理的候选项被显式构建，并通过候选条件化的输入表示进行评估。研究结果表明，该模型在未见评估集上达到了96.88%的准确率、95.92%的宏F1分数、96.83%的加权F1分数和93.94%的二元F1分数。类别层面分析揭示了不对称性能，高/VP附着（N1）的召回率为99.71%，而低/NP/嵌入附着（N2）的召回率为89.26%，表明恢复嵌入解释的难度更大。研究得出结论，形式句法表示可以在基于Transformer的NLP中作为语言结构与上下文神经建模之间的显式接口进行操作，为阿拉伯语句法歧义消解及其他领域提供了一种可控且可解释的方法。
英文摘要
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.
