Technologies Overview for Typo Segregation

Abstract

The article focuses particularly on the difference between typos (accidental mechanical errors) and spelling or conceptual errors that arise from insufficient knowledge of language rules. Modern typo detection methods are analyzed, highlighting the advantages and disadvantages of each. The Levenshtein method is one of the most common algorithms for detecting and correcting errors in text. It effectively identifies and corrects errors in short words where the number of operations to convert the erroneous word to the correct one is small. However, this method does not consider the context in which the word is used, which can lead to incorrect corrections. The keyboard layout-based typo detection method analyzes probable errors that can occur due to the proximity of keys on the keyboard. It is simple to implement and integrate into existing spell-checking systems but does not consider the context of word usage. The contextual analysis method for typo detection relies on using contextual information to identify and correct errors in text, requiring significant computational resources and a large, diverse corpus of texts for effective model training. Deep models, such as BERT or GPT, consider the context of entire sentences or even larger text blocks, allowing for high accuracy in typo detection but require significant computational resources for training and inference, as well as large volumes of high-quality data for training. Machine learning methods, such as n-grams and Bayesian classifiers, show significant potential due to their simplicity and efficiency but may not account for complex dependencies between words and context, reducing their accuracy. The study highlights the importance of accurate error detection in student assessment systems, where typos can affect final grades and the relevance of answers.