# A Systematic Review of Automated Grammar Checking in English Language

arXiv: https://arxiv.org/abs/1804.00540 · HTML: https://mlsanity.com/paper/1804.00540/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Mar 2018.

## Built on by (most cited first, 3 of 3)

- [See What LLMs Cannot Answer: A Self-Challenge Framework for Uncovering LLM Weaknesses](https://mlsanity.com/paper/2408.08978/) (2024)
- [Mis-prompt: Benchmarking Large Language Models for Proactive Error Handling](https://mlsanity.com/paper/2506.00064/) (2025)
- [Zero-Shot Grammar Competency Estimation Using Large Language Model Generated Pseudo Labels](https://mlsanity.com/paper/2511.13152/) (2025)

## Abstract (arXiv)

Grammar checking is the task of detection and correction of grammatical errors in the text. English is the dominating language in the field of science and technology. Therefore, the non-native English speakers must be able to use correct English grammar while reading, writing or speaking. This generates the need of automatic grammar checking tools. So far many approaches have been proposed and implemented. But less efforts have been made in surveying the literature in the past decade. The objective of this systematic review is to examine the existing literature, highlighting the current issues and suggesting the potential directions of future research. This systematic review is a result of analysis of 12 primary studies obtained after designing a search strategy for selecting papers found on the web. We also present a possible scheme for the classification of grammar errors. Among the main observations, we found that there is a lack of efficient and robust grammar checking tools for real time applications. We present several useful illustrations- most prominent are the schematic diagrams that we provide for each approach and a table that summarizes these approaches along different dimensions such as target error types, linguistic dataset used, strengths and limitations of the approach. This facilitates better understandability, comparison and evaluation of previous research.