--- title: "Who cites Annotating Errors in English — 2 citing papers" description: "2 papers of the Elamur Research library cite «Annotating Errors in English Learners' Written Language Production: Advancing Automated…" canonical: "https://research.elamur.ai/free/en/papers/coyne-2025-annotating-errors-english-learners-written/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/coyne-2025-annotating-errors-english-learners-written/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Annotating Errors in English Learners' Written…](https://md.research.elamur.ai/free/en/papers/coyne-2025-annotating-errors-english-learners-written/) / cited by # Who cites «Annotating Errors in English Learners' Written Language Production: Advancing Automated Written Feedback Systems» 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 «Annotating Errors in English Learners' Written Language Production: Advancing Automated Written Feedback Systems»](https://md.research.elamur.ai/free/en/papers/coyne-2025-annotating-errors-english-learners-written/) 1. [**ArabiGEE: A Hierarchical Taxonomy for Arabic Grammatical Error Explanation arXiv:2606.10765**Khaled Elhady, Omar Kallas, Nizar Habash, et al.*2026*](https://md.research.elamur.ai/free/en/papers/elhady-2026-arabigee-hierarchical-taxonomy-arabic-grammatical/) 2. **[Opportunities and Challenges of LLMs in Education: An NLP Perspective](https://md.research.elamur.ai/free/en/papers/vajjala-2026-opportunities-challenges-llms-education-nlp/) [Workshop on Innovative Use of NLP for Building Educational Applications (BEA)](https://md.research.elamur.ai/free/en/venues/bea/)**Sowmya Vajjala, Bashar Alhafni, Stefano Banno, et al.*2026* - [← back to](https://md.research.elamur.ai/free/en/papers/coyne-2025-annotating-errors-english-learners-written/) — Annotating Errors in English Learners' Written Language Production: Advancing Automated Written Feedback Systems