Survey of Automatic Spelling Correction

Hládek, Daniel; Staš, Ján; Pleva, Matúš

doi:10.3390/electronics9101670

Cited by 56 publications

(28 citation statements)

References 137 publications

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“…Spelling error was a form of unusually written word that makes the text harder to read and process (Hládek, Staš, & Pleva, 2020). In general, spelling errors are in the form of typing errors in text which will result in a string experiencing excess, deficiency and change of one or more characters compared to text that matches the lexicon.…”

Section: Literature Review Spelling Errormentioning

confidence: 99%

“…In general, spelling errors are in the form of typing errors in text which will result in a string experiencing excess, deficiency and change of one or more characters compared to text that matches the lexicon. Spelling Error Detection and Correction generally consists of three main stages, namely lexicon preparation, candidate generation, string correction based on intended word and context (Hládek, Staš, & Pleva, 2020). Knowledge and information about the causes of spelling errors are needed in developing spelling error detection and correction models.…”

Section: Literature Review Spelling Errormentioning

confidence: 99%

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Indonesian Spelling Error Detection and Type Identification Using Bigram Vector and Minimum Edit Distance Based Probabilities

Erwina

Tommy

Mayasari

2021

SinkrOn

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Spelling error has become an error that is often found in this era which can be seen from the use of words that tend to follow trends or culture, especially in the younger generation. This study aims to develop and test a detection and identification model using a combination of Bigram Vector and Minimum Edit Distance Based Probabilities. Correct words from error words are obtained using candidates search and probability calculations that adopt the concept of minimum edit distance. The detection results then identified the error type into three types of errors, namely vowels, consonants and diphthongs from the error side on the tendency of the characters used as a result of phonemic rendering at the time of writing. The results of error detection and identification of error types obtained are quite good where most of the error test data can be detected and identified according to the type of error, although there are several detection errors by obtaining more than one correct word as a result of the same probability value of these words.

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Section: Literature Review Spelling Errormentioning

confidence: 99%

Section: Literature Review Spelling Errormentioning

confidence: 99%

Indonesian Spelling Error Detection and Type Identification Using Bigram Vector and Minimum Edit Distance Based Probabilities

Erwina

Tommy

Mayasari

2021

SinkrOn

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“…Scientists in the field of artificial intelligence continue to develop techniques and algorithms that are capable of correcting spelling mistakes by computer programs. One of the challenges of such enterprise is to develop a program in checking and correcting spelling mistakes in an interactive fashion and shortest possible time (Hládek, Staš, & Pleva, 2020). It is why a need is felt to devise research that produces a program in the form of an application to correct spelling mistakes.…”

Section: U-tapis: Automatic Spelling Filter As An Effort To Improve Indonesian Language Competencies Of Journalistic Studentsmentioning

confidence: 99%

“…In line with the study by Hládek et al (2020), scientists in artificial intelligence continue to develop techniques and algorithms that are capable of identifying and correcting mistakes in a computer program. The presence of the U-Tapis aims at responding to these challenges in an interactive way and shortest possible time so that it can be used as a learning media in improving students' competencies in using the Indonesian language.…”

Section: Figure 2 Causes That Make Students Repeatmentioning

confidence: 99%

U-Tapis: Automatic Spelling Filter as an Effort to Improve Indonesian Language Competencies of Journalistic Students

Mediyawati

Young

Nusantara

2021

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The problem of Indonesian language errors among students is of particular observation. This problem becomes an important concern for students majoring in journalism because one day the graduates will become journalists. A language error filtering application has been developed that can be used quickly and accurately in journalists’ work. This application, which involves statistical analysis, computational language, and artificial intelligence, is named U-Tapis. This study was aimed at finding out the feasibility and effectiveness measures of the U-Tapis model by focusing on the language of students’ journalistic works such as opinions, news items, and news articles. The study involved 30 students majoring in Journalism, a private university in Jakarta, Indonesia. It was found that the students’ error rate decreased after the use of the model. It can be concluded that, in addition to eligibility which reaches 92.31%, the U-Tapis application can help effectively increase students’ proficiency in the use of the Indonesian language.

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“…Spelling errors are words that spell-checker could not find in its lexicon [6]. Typo on keywords is one of reasons why the search result is not relevant since keywords entered are not in the database, so the search engine cannot find relevant documents with the typo-indicated keywords.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Spelling Correction and Query Expansion for Relevance Document Searching

Soyusiawaty¹,

Wolley²

2021

IJACSA

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A digital library is a type of information retrieval (IR) system. The existing IR methodologies generally have problems on keyword searching. Some of search engine has not been able to provide search results with partial matching and typographical error. Therefore, it is required to be able to provide search results that are relevant to keywords provided by the user. We proposed a model to solve the problem by combining the spell correction and query expansion. Searching is starting with indexing the title of the document by preprocessing the title of all incoming document data and then weighting the Term Frequency -Inverse Document Frequency (TF-IDF) against all terms of the whole document. Levenshtein Distance algorithm is used in the search process to correct typo-indicated keywords. Before calculating the relevance between the keywords and the documents using Cosine Similarity, the keywords are expanded using Query Expansion to increase number of documents retrieved. Calculation results using Cosine Similarity are then added to Query Expansion weight calculation to get final ranking result. Results show improvements over IR system compared with system without spell check and query expansion. The results of the study in the form of web-based application conducted testing for 50 times with number of data of 2,045. The system was able to correct typo-indicated keywords and search documents with average recall value of 95.91%, average precision value of 63.82% and average Non Interpolated Average Precision (NIAP) value of 86.29%.

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Survey of Automatic Spelling Correction

Cited by 56 publications

References 137 publications

Indonesian Spelling Error Detection and Type Identification Using Bigram Vector and Minimum Edit Distance Based Probabilities

Indonesian Spelling Error Detection and Type Identification Using Bigram Vector and Minimum Edit Distance Based Probabilities

U-Tapis: Automatic Spelling Filter as an Effort to Improve Indonesian Language Competencies of Journalistic Students

Hybrid Spelling Correction and Query Expansion for Relevance Document Searching

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