使用ISO语言标记框架和TEI Lex-0分析与编码Al-Mawrid阿拉伯语-英语词典
Analyzing and Encoding the Al-Mawrid Arabic-English Dictionary with the ISO Language Markup Framework and TEI Lex-0
AI总结：
本文提出了一种系统化方法，将Al-Mawrid阿拉伯语-英语词典数字化并编码为标准化计算词典，采用ISO LMF和TEI Lex-0双重标准，实现91%的结构解析准确率。
摘要：
本文提出了一种稳健的方法，用于系统化数字化和编码Al-Mawrid阿拉伯语-英语词典，将其从传统的印刷资源转变为标准化的计算词典。针对阿拉伯语词汇基础设施中的显著空白，本研究采用双重标准框架，将ISO词汇标记框架（LMF）与文本编码倡议TEI Lex-0指南对齐。通过应用编辑视角处理词典的宏观和微观结构，研究解决了20世纪双语词典中典型的结构歧义和标点不一致问题。该方法基于对词典词汇知识密度的实证分析。基于代表性样本（字母Ayn，占总量的4.6%），研究为编码过程提供了科学依据，展示了91%的结构解析准确率。信息提取规则的定量评估显示出高性能，同义词的精确率为85%，召回率为98%，其他形态语义特征的精确率为88%。除了技术描述，本文还与现有阿拉伯语词汇资源进行了批判性比较，并讨论了TEI Lex-0在建模特定阿拉伯语现象（如隐式“开放集”语义关系和分散的形态线索）时的局限性。此外，研究通过建立可扩展的基于前缀的引用系统，探索了语言关联开放数据（LLOD）集成的潜力，促进了该资源在语义网中的包含。最终成果是一个可互操作、机器可处理的资源，为阿拉伯语自然语言处理和数字人文社区中复杂遗留双语词典的逆向数字化提供了可复现的工作流程。
英文摘要：
This paper presents a robust methodology for the systematic digitization and encoding of the Al-Mawrid Arabic-English dictionary, transforming it from a legacy print resource into a standardized computational lexicon. Addressing a significant gap in Arabic lexical infrastructure, the study adopts a dual-standard framing that aligns the ISO Lexical Markup Framework (LMF) with the Text Encoding Initiative TEI Lex-0 guidelines. By applying an editorial view to the dictionary's macro- and microstructure, the research resolves the structural ambiguities and punctuation inconsistencies typical of 20th-century bilingual dictionaries. The methodology is grounded in an empirical analysis of the dictionary's lexical knowledge density. Drawing on a representative sample (the letter Ayn, comprising 4.6% of the total volume), the study provides scientific weight to the encoding process, demonstrating a structural parsing accuracy of 91%. Quantitative evaluation of the information extraction rules reveals high performance, with 85% precision and 98% recall for synonyms, and 88% precision for other morpho-semantic features. Beyond technical description, the paper provides a critical comparison with existing Arabic lexical resources and discusses the limitations of TEI Lex-0 when modelling specific Arabic phenomena, such as implicit "open set" semantic relations and scattered morphological cues. Furthermore, the study explores the potential for Linguistic Linked Open Data (LLOD) integration by establishing a scalable prefix-based referencing system that facilitates the resource's inclusion in the semantic web. The result is an interoperable, machine-tractable resource that provides a reproducible workflow for the retro-digitization of complex legacy bilingual lexicons within the Arabic NLP and Digital Humanities communities.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
