Revised N-Gram based Automatic Spelling Correction Tool to Improve Retrieval Effectiveness

Ahmed, Farag; Luca, Ernesto William De; Nürnberger, Andreas

doi:10.17562/pb-40-6

Cited by 32 publications

(21 citation statements)

References 16 publications

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“…There are many algorithmic techniques for detecting and correcting spelling errors in text [23]. Error Correction Approaches like Neural Based [24], Levenshtein Edit Distance [25], Similarity Keys [22], Rule-Based [26], Probabilistic [22], and N-gram [27].…”

Section: The Methods 31 the Description Of The Methodsmentioning

confidence: 99%

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Automatic Spelling Correction based on n-Gram Model

Atawy¹,

ElGhany²

2018

IJCA

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A spell checker is a basic requirement for any language to be digitized. It is a software that detects and corrects errors in a particular language. This paper proposes a model to spell error detection and auto-correction that is based on n-gram technique and it is applied in error detection and correction in English as a global language. The proposed model provides correction suggestions by selecting the most suitable suggestions from a list of corrective suggestions based on lexical resources and n-gram statistics. It depends on a lexicon of Microsoft words. The evaluation of the proposed model uses English standard datasets of misspelled words. Error detection, automatic error correction, and replacement are the main features of the proposed model. The results of the experiment reached approximately 93% of accuracy and acted similarly to Microsoft Word as well as outperformed both of Aspell and Google.

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Section: The Methods 31 the Description Of The Methodsmentioning

confidence: 99%

“…This information can be presented in data structures, from plain lists to complicated with many types of linguistic information and relations associated with the entries stored in the resource [23] [32]. Lexical resources are used for language and knowledge engineering.…”

Section: The Methods 31 the Description Of The Methodsmentioning

confidence: 99%

Automatic Spelling Correction based on n-Gram Model

Atawy¹,

ElGhany²

2018

IJCA

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“…Larger sizes are sometimes referred to by the value of n, such as four-gram, five-gram, and so on. One main advantage of the n-gram method is that it is language independent [13].…”

Section: Spell Checkermentioning

confidence: 99%

A Systematic Literature Review on Spell Checkers for Bangla Language

Mandal¹,

Hossain²

2017

IJMECS

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Abstract-Spell checkers check whether a word is misspelled and provide suggestions to correct it. Detection and correction of spelling errors in Bangla language which is the seventh most spoken native language in the world, is very onerous because of the complex rules of Bangla spelling. There is no systematic literature review on this research topic. In this paper, we present a systematic literature review on checking and correcting spelling errors in Bangla language. We investigate the current methods used for spell checking and find out what challenges are addressed by those methods. We also report the limitations of those methods. Recent relevant studies are selected based on a set of significant criteria. Our results indicate that there are research gaps in this research topic and has a potential for further investigation.

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“…Several studies have been performed on the detection of word writing errors, for example in the form of automatic spell checker applications. Automatic spell checkers have been carried out since the 1960s and have been developed to date [3]. The aim is to improve and revise the quality of detection results, and utilize and develop knowledge related to what methods are appropriate to make the system works.…”

Section: Introductionmentioning

confidence: 99%

Audit System Development for Government Institution Documents Using Stream Deep Learning to Support Smart Governance

Cholissodin

Soebroto

Sutrisno

2019

JITeCS

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Document audit system is a means of evaluating documents on the results of delivering information, administrative documentary evidence in the form of texts or others. Currently, these activities become easier with the presence of computer technology, smartphones, and the internet. One of the examples is the documents created by various government institutions whether local, city and central government. The instance is online-published documents that are shaded by certain government institutions. Before the documents are published or used as an archive or authentic evidence for reporting or auditing activities, the documents must go through the editing stage to correct if there are errors and deficiencies such as spelling errors or incomplete information. In the editing process, however, a person may not be able to escape from making mistakes that result in the existence of writing errors after the editing process before the submission. Word spelling mistakes can change the meaning of the conveyed knowledge and cause misunderstanding of information to the readers, especially for assessors or the audit team. Based on the problem, the researcher intends to assist the work of the audit preparation team in document analysis by proposing a system capable of detecting word spelling errors using the Dictionary Lookup method from Information Retrieval (IR) and Natural Language Processing (NLP) science combined with Stream Deep Learning algorithms. Dictionary Lookup method is considered effective in determining the spelling of words that are true or false based on Lexical Resource. In addition, String Matching method that has been developed can correct word-writing errors correctly and quickly.Keywords: spelling mistake detection, dictionary lookup, audit of government institution documents, stream deep learning

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Revised N-Gram based Automatic Spelling Correction Tool to Improve Retrieval Effectiveness

Cited by 32 publications

References 16 publications

Automatic Spelling Correction based on n-Gram Model

Automatic Spelling Correction based on n-Gram Model

A Systematic Literature Review on Spell Checkers for Bangla Language

Audit System Development for Government Institution Documents Using Stream Deep Learning to Support Smart Governance

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