← 研踪 PaperTrailACL Anthology · ACL 2026:acl:2026.acl-long.1541 · 记录 #1
AdabNER: Arabic Digital Archive Books with Nested Entity Recognition
来源日期（仅到月份） 2026-07
来源记录日期 2026-07
雷达观测 2026-11-01 21:53
摘要
Most studies on Arabic Named Entity Recognition (NER) have focused on news texts and social media posts, while the large and rich corpus of literary Arabic books has been underrepresented. We introduce AdabNER, the first large-scale nested NER dataset for Modern Standard Arabic (MSA) literary texts, comprising the first 6,000 words annotated from each of 138 books spanning ten literary genres, including history, biography, literary criticism, and travel literature, and covering works from the 1880s to the 2020s. The corpus comprises about 876K tokens, manually annotated using a nested 21 entity tag annotation scheme, yielding 78,530 entity mentions, 18.96% of which are nested. We fine-tuned five pre-trained Arabic BERT encoders in two settings: stratified and leave-book-out, achieving F1 scores of 0.86 and 0.83 with AraBERTv2, respectively. We also evaluated five large language models through few-shot in-context learning, including open-source models and the closed-source Gemini 3 Pro, with Gemini 3 Pro achieving the highest LLM F1 score of 0.59. Supervised results degraded under out-of-domain evaluation; however, joint multi-domain training reduced this gap to less than a 1% F1 loss, demonstrating that domain-diverse training data is key to robust Arabic NER, though broader validation beyond the experiments reported is needed. AdabNER and its annotation guidelines are publicly available at https://doi.org/10.5281/zenodo.19468385.
论文发现出处
这些页面提供论文线索，不代表同行评审或质量认证。
观测到的版本
平台记录号代表本站观测到的元数据变化，不是作者修订号。来源日期仅到月份或年份时，按该月或该年首日存档。
记录 #1 · 2026-07 · 最新观测版本
AdabNER: Arabic Digital Archive Books with Nested Entity Recognition
Most studies on Arabic Named Entity Recognition (NER) have focused on news texts and social media posts, while the large and rich corpus of literary Arabic books has been underrepresented. We introduce AdabNER, the first large-scale nested NER dataset for Modern Standard Arabic (MSA) literary texts, comprising the first 6,000 words annotated from each of 138 books spanning ten literary genres, including history, biography, literary criticism, and travel literature, and covering works from the 1880s to the 2020s. The corpus comprises about 876K tokens, manually annotated using a nested 21 entity tag annotation scheme, yielding 78,530 entity mentions, 18.96% of which are nested. We fine-tuned five pre-trained Arabic BERT encoders in two settings: stratified and leave-book-out, achieving F1 scores of 0.86 and 0.83 with AraBERTv2, respectively. We also evaluated five large language models through few-shot in-context learning, including open-source models and the closed-source Gemini 3 Pro, with Gemini 3 Pro achieving the highest LLM F1 score of 0.59. Supervised results degraded under out-of-domain evaluation; however, joint multi-domain training reduced this gap to less than a 1% F1 loss, demonstrating that domain-diverse training data is key to robust Arabic NER, though broader validation beyond the experiments reported is needed. AdabNER and its annotation guidelines are publicly available at https://doi.org/10.5281/zenodo.19468385.
查看此版本解读 →查看此版本原文 ↗