A Comprehensive Approach to Misspelling Correction with BERT and Levenshtein Distance

TL;DR

This paper presents a novel method combining BERT and Levenshtein distance to effectively identify and correct various spelling errors in Persian text, demonstrating superior performance over existing systems.

Contribution

It introduces a combined approach leveraging BERT and Levenshtein distance for improved spelling correction, with a comprehensive dataset for Persian language errors.

Findings

High accuracy in spelling correction

Outperforms existing Persian spelling correction systems

Effective handling of non-word and real-word errors

Abstract

Writing, as an omnipresent form of human communication, permeates nearly every aspect of contemporary life. Consequently, inaccuracies or errors in written communication can lead to profound consequences, ranging from financial losses to potentially life-threatening situations. Spelling mistakes, among the most prevalent writing errors, are frequently encountered due to various factors. This research aims to identify and rectify diverse spelling errors in text using neural networks, specifically leveraging the Bidirectional Encoder Representations from Transformers (BERT) masked language model. To achieve this goal, we compiled a comprehensive dataset encompassing both non-real-word and real-word errors after categorizing different types of spelling mistakes. Subsequently, multiple pre-trained BERT models were employed. To ensure optimal performance in correcting misspelling errors, we propose a combined approach utilizing the BERT masked language model and Levenshtein distance. The results from our evaluation data demonstrate that the system presented herein exhibits remarkable capabilities in identifying and rectifying spelling mistakes, often surpassing existing systems tailored for the Persian language.