--- title: "Who cites Adapting Standard Open-Source — 2 citing papers" description: "2 papers of the Elamur Research library cite «Adapting Standard Open-Source Resources To Tagging A Morphologically Rich Language: A…" canonical: "https://research.elamur.ai/free/en/papers/rabiee-2011-adapting-standard-open-source-resources/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/rabiee-2011-adapting-standard-open-source-resources/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Adapting Standard Open-Source Resources To…](https://md.research.elamur.ai/free/en/papers/rabiee-2011-adapting-standard-open-source-resources/) / cited by # Who cites «Adapting Standard Open-Source Resources To Tagging A Morphologically Rich Language: A Case Study With Arabic» 2 papers of our library cite it (of the 149,439 whose bibliographies we read) · 2 with a page here, the most cited first · [← back to «Adapting Standard Open-Source Resources To Tagging A Morphologically Rich Language: A Case Study With Arabic»](https://md.research.elamur.ai/free/en/papers/rabiee-2011-adapting-standard-open-source-resources/) 1. [**Statistical Parsing by Machine Learning from a Classical Arabic Treebank**Kais Dukes*2015 · 27 citations*](https://md.research.elamur.ai/free/en/papers/dukes-2015-statistical-parsing-machine-learning-classical/) 2. **[MemeMind at ArAIEval Shared Task: Spotting Persuasive Spans in Arabic Text with Persuasion Techniques Identification](https://md.research.elamur.ai/free/en/papers/biswas-2024-mememind-araieval-shared-task-spotting/) [Arabic Natural Language Processing Conference (ArabicNLP)](https://md.research.elamur.ai/free/en/venues/arabicnlp/)**Md. Rafiul Biswas, Zubair Shah, Wajdi Zaghouani*2024 · 1 citation* - [← back to](https://md.research.elamur.ai/free/en/papers/rabiee-2011-adapting-standard-open-source-resources/) — Adapting Standard Open-Source Resources To Tagging A Morphologically Rich Language: A Case Study With Arabic