# Classifying Syntactic Errors in Learner Language

arXiv: https://arxiv.org/abs/2010.11032 · HTML: https://mlsanity.com/paper/2010.11032/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Oct 2020.

## Built on by (most cited first, 11 of 11)

- [Grammatical Error Correction: A Survey of the State of the Art](https://mlsanity.com/paper/2211.05166/) (2022)
- [The Grammar-Learning Trajectories of Neural Language Models](https://mlsanity.com/paper/2109.06096/) (2021)
- [Advancements in Arabic Grammatical Error Detection and Correction: An Empirical Investigation](https://mlsanity.com/paper/2305.14734/) (2023)
- [Automatic Error Type Annotation for Arabic](https://mlsanity.com/paper/2109.08068/) (2021)
- [Revisiting Grammatical Error Correction Evaluation and Beyond](https://mlsanity.com/paper/2211.01635/) (2022)
- [SERRANT: a syntactic classifier for English Grammatical Error Types](https://mlsanity.com/paper/2104.02310/) (2021)
- [GrASP: A Library for Extracting and Exploring Human-Interpretable Textual Patterns](https://mlsanity.com/paper/2104.03958/) (2021)
- [Opportunities and Challenges of LLMs in Education: An NLP Perspective](https://mlsanity.com/paper/2507.22753/) (2025)
- [Error syntax aware augmentation of feedback comment generation dataset](https://mlsanity.com/paper/2212.14293/) (2022)
- [Some Grammatical Errors are Frequent, Others are Important](https://mlsanity.com/paper/2205.05730/) (2022)
- [ArabiGEE: A Hierarchical Taxonomy for Arabic Grammatical Error Explanation](https://mlsanity.com/paper/2606.10765/) (2026)

## Abstract (arXiv)

We present a method for classifying syntactic errors in learner language, namely errors whose correction alters the morphosyntactic structure of a sentence. The methodology builds on the established Universal Dependencies syntactic representation scheme, and provides complementary information to other error-classification systems. Unlike existing error classification methods, our method is applicable across languages, which we showcase by producing a detailed picture of syntactic errors in learner English and learner Russian. We further demonstrate the utility of the methodology for analyzing the outputs of leading Grammatical Error Correction (GEC) systems.