rwse-checker 0.0.3

Finding and correcting real-word spelling errors using masked language models.

rwse-checker

Real-word spelling errors (RWSEs) pose special challenges for detection methods, as they ‘hide’ in the form of another existing word and in many cases even fit in syntactically. rwse-checker is a modern Transformer-based implementation of earlier probabilistic methods based on confusion sets. It detects RWSEs with a good balance between missing errors and raising too many false alarms. The confusion sets are dynamically configurable, allowing teachers to easily adjust which errors trigger feedback.

Example Usage

from rwse import RWSE_Checker 

checker = RWSE_Checker()
checker.set_confusion_sets([['their','there'],['to','too','two']])

print(checker.check("there", "I want to buy [MASK] cars."))
print(checker.check("too", "I want [MASK] buy their cars."))

which yields

('their', 0.003510827198624611)
('to', 0.9989504218101501)

Citation

If you are using this tool, please cite Transformer-Based Real-Word Spelling Error Feedback with Configurable Confusion Sets (Zesch et al., BEA 2025)

The experimental code for this paper is found in https://github.com/zesch/rwse-experiments

@inproceedings{zesch-etal-2025-transformer,
    title = "Transformer-Based Real-Word Spelling Error Feedback with Configurable Confusion Sets",
    author = "Zesch, Torsten  and
      Gardner, Dominic  and
      Bexte, Marie",
    booktitle = "Proceedings of the 20th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.bea-1.29/",
    doi = "10.18653/v1/2025.bea-1.29",
    pages = "375--383",
    ISBN = "979-8-89176-270-1",
}