Evaluation of text correction using a combination of Levenshtein distance and Trie algorithm

Abstract

Nowadays, technology is advancing with various applications, especially in text processing, such as news recommendations, sentiment analysis, automatic scoring, and language translation. In some cases, spelling errors often occur when inputting text for the translation process, necessitating text correction methods to display suggestions as a result. Therefore, the problem statement raised is about how to improve the accuracy of text correction and evaluate the translation quality at the word level after correcting input text in the context of translation from Indonesian to English. This research aims to develop and evaluate the combination of Levenshtein distance algorithm and Trie to correct input text and evaluate the translation quality at the word level after correcting text. There are various text correction methods, such as Hamming distance, Levenshtein distance, Damerau-Levenshtein distance, and N-Gram. Among several text correction methods. Levenshtein distance algorithm is commonly used to calculate the distance between texts and can be enhanced by using a Trie for more efficient computation in evaluating text correction. This research method resulted in an accuracy of 82.25% with an F1 score of 84.39%, where the developed text correction model produced a good translation using BLEU score with an increase of 1.55% after text correction.