The Context in Automatic Spell Correction

Nejja, Mohammed; Yousfi, Abdellah

doi:10.1016/j.procs.2015.12.055

Cited by 11 publications

(5 citation statements)

References 4 publications

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“…To this end, the algorithm uses a matrix of (n+1)*(m+1) dimension, where n and m are the two previous strings' lengths. The calculation of the cell M[N,P] is equal to the minimum value between the executed elementary operations [8].…”

Section: Methods 21 Levenshtein Distancementioning

confidence: 99%

“…Besides these features are able to give advice that users need without having to be written as a whole [3]. Automatic spell checker systems aim to verify and correct erroneous words through a suggested set of words that are the nearest lexically to the erroneous ones [8]. The spell check can be divided into two main subproblems: the error verification and the correction of errors found.…”

Section: Introductionmentioning

confidence: 99%

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Autocomplete and Spell Checking Levenshtein Distance Algorithm To Getting Text Suggest Error Data Searching In Library

Yulianto

Arifudin

Alamsyah

2018

SJI

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Nowadays internet technology provide more convenience for searching information on a daily. Users are allowed to find and publish their resources on the internet using search engine. Search engine is a computer program designed to facilitate a user to find the information or data that they need. Search engines generally find the data based on keywords they entered, therefore a lot of case when the user can’t find the data that they need because there are an error while entering a keyword. Thats why a search engine with the ability to detect the entered words is required so the error can be avoided while we search the data. The feature that used to provide the text suggestion is autocomplete and spell checking using Levenshtein distance algorithm. The purpose of this research is to apply the autocomplete feature and spell checking with Levenshtein distance algorithm to get text suggestion in an error data searching in library and determine the level of accuracy on data search trials. This research using 1155 data obtained from UNNES Library. The variables are the input process and the classification of books. The accuracy of Levenshtein algorithm is 86% based on 1055 source case and 100 target case.

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Section: Methods 21 Levenshtein Distancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Autocomplete and Spell Checking Levenshtein Distance Algorithm To Getting Text Suggest Error Data Searching In Library

Yulianto

Arifudin

Alamsyah

2018

SJI

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“…Our objectives in this research work were to improve the scheduling rate and the precision rate in the edit distance based correction process [14]- [16], as well as to integrate the level of morphological analysis in the spelling correction phase [17], as well as taking into consideration the context in the correction process [18]. For a decade, our research team has consistently presented a series of relevant approaches in the field of spelling correction for errors made in typing Arabic texts [19], [20].…”

Section: Introductionmentioning

confidence: 99%

Improving SpellChecking: an effective Ad-Hoc probabilistic lexical measure for general typos

Gueddah

Nejja

Iazzi

et al. 2022

IJEECS

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Since the era of learning to write by human beings, mistakes made in typing words have occupied a privileged place in linguistic studies, integrating new disciplines into school curricula such as spelling and dictation. According to exhaustive studies that we have done in the field of spellchecking errors made in typing Arabic texts, very few research works that deal with typographical errors specifically caused by the insertion or missing of the blank-space in words. On the other hand, spelling correction software remains ineffective for handling this type of errors. Failure to process errors due to the insertion/missing of blankspace between and in words leads and brings us back to situations of ambiguity and incomprehension of the meaning of the typed text. To remedy this limitation of correction, we propose in this article an ad-hoc probabilistic method which is based jointly on two approaches. The first approach treats the errors due to deletion or missing of blank-space between or inside words, while the second puts emphasis in correcting space insertion errors in a word of course in addition to other kinds of elementary editing errors (addition, deletion, permutation of characters). Our new approach combines edit distance with n-gram language models to correct the errors already mentioned. Our new approach gave an accuracy rate that reaches 98,14% for missing blank-space errors (noted MBSE) and 89,5% for insertion blank-dpace errors (noted IBSE), which gives an average correction rate of around 95,26%. These results are very encouraging and show the interest and the importance of our approach.

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“…At the point of writing, no formal taxonomy on Malay social media text has been published. Instead, narrow categorisations were found described within works in text normalization and automatic spell checker [20,25]. Categorisations appear to be cherry-picked in light of the solution proposed.…”

Section: Introductionmentioning

confidence: 99%

A taxonomy of Malay social media text

Maskat

Munarko

2019

IJEECS

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In this paper, we proposed a preliminary taxonomy of Malay social media text. Performing text analytics on Malay social media text is a challenge. The formal Malay language follows specific spelling and sentence construction rules. However, the Malay language used in social media differs in both aspects. This impedes the accuracy of text analytics. Due to the complexity of Malay social media text, many researches has chosen to focus on classifying the formal Malay language. To the best of our knowledge, we are the first to propose a formal taxonomy for Malay text in social media. Narrow and informal categorisations of Malay social media text can be found amidst efforts to pre-process social media text, yet cherry-picked only some categories to be handled. We have differentiated Malay social media text from the formal Malay language by identifying them as Social Media Malay Language or SMML. They consists of spelling variations, Malay-English mix sentence, Malay-spelling English words, slang-based words, vowel-les words, number suffixes and manner of expression.This taxonomy is expected to serve as a guideline in research and commercial products.

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The Context in Automatic Spell Correction

Cited by 11 publications

References 4 publications

Autocomplete and Spell Checking Levenshtein Distance Algorithm To Getting Text Suggest Error Data Searching In Library

Autocomplete and Spell Checking Levenshtein Distance Algorithm To Getting Text Suggest Error Data Searching In Library

Improving SpellChecking: an effective Ad-Hoc probabilistic lexical measure for general typos

A taxonomy of Malay social media text

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