A Context Free Spell Correction Method using Supervised Machine Learning Algorithms

Yunus, Ahmed; Masum,

doi:10.5120/ijca2020920288

Cited by 5 publications

(3 citation statements)

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“…In this paper, we have taken several reference journals and articles as references in our research. Spelling checker is very helpful in the process of eliminating slang words in the dataset in the process of sentiment analysis [5], [9]. Ababneh [11] explain that Naive Bayes is better than decition tree and k-NN.…”

Section: Related Workmentioning

confidence: 99%

“…Wordnet can calculate and expand the meaning of some words that have the same meaning as synonyms and antonyms [8]. Spelling checker is a process of detecting spelling errors or slang words in word writing using an existing language dictionary [9]. The spelling checker is very helpful in this research because can help recognize spelling errors or slang words.…”

Section: Introductionmentioning

confidence: 99%

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Effects of using wordnet and spelling checker on classification methods in sentiment analysis for datasets using Bahasa

Andika

Suharjito

2022

IJEECS

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<span>Sentiment analysis was a system for recognizing and extracting opinions in documents. There were two weaknesses in sentiment analysis. The first weakness was preprocessing in sentiment analysis can’t recognize slang words so that important words that should have been recognized became unrecognizable. The Second was the feature extraction process in sentiment analysis only recognized words based on the form of the word but can’t recognize the similar word. In this paper, we proposed spelling checker and wordnet to fix these weaknesses. We also used k-nearest neighbor (KNN), Naïve Bayes, and decision tree as methods for check classify the text. The purpose of this research was first to know the effects of used Wordnet and spelling checkers in sentiment analysis and second was to improve the data processing process in the existing sentiment analysis. The dataset that we used in the research was a list of tweets in Bahasa. The results showed wordnet and spelling checker make a decrease in the valued of false positives, false negatives, and true negatives in the calculation of the confusion matrix. It can increase the accuracy of the K-NN from 43% to 72%, Naïve Bayes from 41% to 71% and decision tree from 47% to 75%.</span>

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Effects of using wordnet and spelling checker on classification methods in sentiment analysis for datasets using Bahasa

Andika

Suharjito

2022

IJEECS

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“…Exemplos já mencionados anteriormente são o Soundex e o Metaphone. Ainda, utilizam-se abordagens baseadas em regras de geração de candidatos (YANNAKOUDAKIS; FAWTHROP, 1983a,b), n-gramas (KIM; SHAWE-TAYLOR, 1992, probabilidade (KERNIGHAN; CHURCH; GALE, 1990;JURAFSKY;MARTIN, 2008;INGELS, 1996) e, recentemente, redes neurais artificiais (DEFFNER; EDER; GEI- GER, 1990;HODGE;AUSTIN, 2002AUSTIN, , 2003YUNUS;MASUM, 2020).…”

Section: Desenvolvimento De Um Corretor Ortográficounclassified

Desenvolvimento de um corretor ortográfico

2021

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Corretores ortográficos são ferramentas computacionais utilizadas cotidianamente na redação de textos e demensagens ou, de forma oculta, na busca por informação e mineração de dados. Diante de sua relevância,o presente trabalho apresenta o percurso histórico de desenvolvimento dos corretores ortográficos e ilustracomo, de forma simples, é possível criar um corretor ortográfico eficiente a partir da proposta de Norvig (2007). Salientam-se, também, algumas ferramentas e as estratégias empregadas na elaboração de corretores,como a remoção de afixos e a computação de n-gramas. Explicita-se, ainda, a implementação do corretorortográfico de Norvig (2007) e verifica-se seu desempenho na tarefa de correção automática em diferentesconjuntos de dados de erros ortográficos. Expõe-se, também, uma comparação na performance de um corretorortográfico que se vale da remoção de afixos em relação a um corretor que não adota semelhante estratégia.

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Automated Spelling Error Detection in Assamese Texts using Deep Learning Approaches

Phukan,

Neog,

Goutom

et al. 2024

Procedia Computer Science

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A Context Free Spell Correction Method using Supervised Machine Learning Algorithms

Cited by 5 publications

References 3 publications

Effects of using wordnet and spelling checker on classification methods in sentiment analysis for datasets using Bahasa

Effects of using wordnet and spelling checker on classification methods in sentiment analysis for datasets using Bahasa

Desenvolvimento de um corretor ortográfico

Automated Spelling Error Detection in Assamese Texts using Deep Learning Approaches

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