# Context-Sensitive Malicious Spelling Error Correction

arXiv: https://arxiv.org/abs/1901.07688 · HTML: https://mlsanity.com/paper/1901.07688/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jan 2019.

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

- [VSEC: Transformer-based Model for Vietnamese Spelling Correction](https://mlsanity.com/paper/2111.00640/) (2021)
- [SWE2: SubWord Enriched and Significant Word Emphasized Framework for Hate Speech Detection](https://mlsanity.com/paper/2409.16673/) (2024)
- [Breaking the Cloak! Unveiling Chinese Cloaked Toxicity with Homophone Graph and Toxic Lexicon](https://mlsanity.com/paper/2505.22184/) (2025)

## Abstract (arXiv)

Misspelled words of the malicious kind work by changing specific keywords and are intended to thwart existing automated applications for cyber-environment control such as harassing content detection on the Internet and email spam detection. In this paper, we focus on malicious spelling correction, which requires an approach that relies on the context and the surface forms of targeted keywords. In the context of two applications--profanity detection and email spam detection--we show that malicious misspellings seriously degrade their performance. We then propose a context-sensitive approach for malicious spelling correction using word embeddings and demonstrate its superior performance compared to state-of-the-art spell checkers.