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According to exhaustive" /> <title>(PDF) Improving SpellChecking: an effective Ad-Hoc probabilistic lexical measure for general typos | Indonesian Journal of Electrical Engineering and Computer Science - Academia.edu</title> <link rel="canonical" href="https://www.academia.edu/89754980/Improving_SpellChecking_an_effective_Ad_Hoc_probabilistic_lexical_measure_for_general_typos" /> <script async src="https://www.googletagmanager.com/gtag/js?id=G-5VKX33P2DS"></script> <script> window.dataLayer = window.dataLayer || []; function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); gtag('config', 'G-5VKX33P2DS', { cookie_domain: 'academia.edu', send_page_view: false, }); gtag('event', 'page_view', { 'controller': "single_work", 'action': "show", 'controller_action': 'single_work#show', 'logged_in': 'false', 'edge': 'unknown', // Send nil if there is no A/B test bucket, in case some records get logged // with missing data - that way we can distinguish between the two cases. // ab_test_bucket should be of the form <ab_test_name>:<bucket> 'ab_test_bucket': null, }) </script> <script> var $controller_name = 'single_work'; var $action_name = "show"; var $rails_env = 'production'; var $app_rev = '49879c2402910372f4abc62630a427bbe033d190'; var $domain = 'academia.edu'; var $app_host = "academia.edu"; var $asset_host = "academia-assets.com"; var $start_time = new Date().getTime(); var $recaptcha_key = "6LdxlRMTAAAAADnu_zyLhLg0YF9uACwz78shpjJB"; var $recaptcha_invisible_key = "6Lf3KHUUAAAAACggoMpmGJdQDtiyrjVlvGJ6BbAj"; var $disableClientRecordHit = false; </script> <script> window.require = { config: function() { return function() {} } } </script> <script> window.Aedu = window.Aedu || {}; window.Aedu.hit_data = null; window.Aedu.serverRenderTime = new Date(1732485251000); window.Aedu.timeDifference = new Date().getTime() - 1732485251000; </script> <script type="application/ld+json">{"@context":"https://schema.org","@type":"ScholarlyArticle","abstract":"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.","author":[{"@context":"https://schema.org","@type":"Person","name":"Indonesian Journal of Electrical Engineering and Computer Science"}],"contributor":[],"dateCreated":"2022-11-01","dateModified":"2022-11-02","datePublished":"2022-01-01","headline":"Improving SpellChecking: an effective Ad-Hoc probabilistic lexical measure for general typos","inLanguage":"en","keywords":["Spelling","Corrections","Probability","Blank spaces"],"locationCreated":null,"publication":"Indonesian Journal of Electrical Engineering and Computer Scienc","publisher":{"@context":"https://schema.org","@type":"Organization","name":null},"image":null,"thumbnailUrl":null,"url":"https://www.academia.edu/89754980/Improving_SpellChecking_an_effective_Ad_Hoc_probabilistic_lexical_measure_for_general_typos","sourceOrganization":[{"@context":"https://schema.org","@type":"EducationalOrganization","name":null}]}</script><link 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"https://www.academia.edu/login?post_login_redirect_url=https%3A%2F%2Fwww.academia.edu%2F89754980%2FImproving_SpellChecking_an_effective_Ad_Hoc_probabilistic_lexical_measure_for_general_typos%3Fauto%3Ddownload"; window.loswp.translateUrl = "https://www.academia.edu/login?post_login_redirect_url=https%3A%2F%2Fwww.academia.edu%2F89754980%2FImproving_SpellChecking_an_effective_Ad_Hoc_probabilistic_lexical_measure_for_general_typos%3Fshow_translation%3Dtrue"; window.loswp.previewableAttachments = [{"id":93508996,"identifier":"Attachment_93508996","shouldShowBulkDownload":false}]; window.loswp.shouldDetectTimezone = true; window.loswp.shouldShowBulkDownload = true; window.loswp.showSignupCaptcha = false window.loswp.willEdgeCache = false; window.loswp.work = {"work":{"id":89754980,"created_at":"2022-11-01T23:24:39.580-07:00","from_world_paper_id":null,"updated_at":"2022-11-02T12:12:05.372-07:00","_data":{"doi":"10.11591/ijeecs.v27.i1.pp521-527","abstract":"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\nmodels 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.","publication_date":"2022,,","publication_name":"Indonesian Journal of Electrical Engineering and Computer Scienc"},"document_type":"paper","pre_hit_view_count_baseline":null,"quality":"high","language":"en","title":"Improving SpellChecking: an effective Ad-Hoc probabilistic lexical measure for general typos","broadcastable":false,"draft":false,"has_indexable_attachment":true,"indexable":true}}["work"]; window.loswp.workCoauthors = [184756992]; window.loswp.locale = "en"; window.loswp.countryCode = "SG"; window.loswp.cwvAbTestBucket = ""; window.loswp.designVariant = "ds_vanilla"; window.loswp.fullPageMobileSutdModalVariant = "full_page_mobile_sutd_modal"; window.loswp.useOptimizedScribd4genScript = false; window.loswp.appleClientId = 'edu.academia.applesignon';</script><script defer="" src="https://accounts.google.com/gsi/client"></script><div class="ds-loswp-container"><div class="ds-work-card--grid-container"><div class="ds-work-card--container js-loswp-work-card"><div class="ds-work-card--cover"><div class="ds-work-cover--wrapper"><div class="ds-work-cover--container"><button class="ds-work-cover--clickable js-swp-download-button" data-signup-modal="{"location":"swp-splash-paper-cover","attachmentId":93508996,"attachmentType":"pdf"}"><img alt="First page of “Improving SpellChecking: an effective Ad-Hoc probabilistic lexical measure for general typos”" class="ds-work-cover--cover-thumbnail" src="https://0.academia-photos.com/attachment_thumbnails/93508996/mini_magick20221102-1-u69xvt.png?1667370761" /><img alt="PDF Icon" class="ds-work-cover--file-icon" src="//a.academia-assets.com/assets/single_work_splash/adobe.icon-574afd46eb6b03a77a153a647fb47e30546f9215c0ee6a25df597a779717f9ef.svg" /><div class="ds-work-cover--hover-container"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span><p>Download Free PDF</p></div><div class="ds-work-cover--ribbon-container">Download Free PDF</div><div class="ds-work-cover--ribbon-triangle"></div></button></div></div></div><div class="ds-work-card--work-information"><h1 class="ds-work-card--work-title">Improving SpellChecking: an effective Ad-Hoc probabilistic lexical measure for general typos</h1><div class="ds-work-card--work-authors ds-work-card--detail"><a class="ds-work-card--author js-wsj-grid-card-author ds2-5-body-md ds2-5-body-link" data-author-id="184756992" href="https://independent.academia.edu/ijeecsteam"><img alt="Profile image of Indonesian Journal of Electrical Engineering and Computer Science" class="ds-work-card--author-avatar" src="https://0.academia-photos.com/184756992/71977395/60433168/s65_indonesian_journal_of_electrical_engineering_and.science.jpeg" />Indonesian Journal of Electrical Engineering and Computer Science</a></div><p class="ds-work-card--detail ds2-5-body-sm">2022, Indonesian Journal of Electrical Engineering and Computer Scienc</p><p class="ds-work-card--work-abstract ds-work-card--detail ds2-5-body-md">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.</p><div class="ds-work-card--button-container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--work-card","attachmentId":93508996,"attachmentType":"pdf","workUrl":"https://www.academia.edu/89754980/Improving_SpellChecking_an_effective_Ad_Hoc_probabilistic_lexical_measure_for_general_typos"}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--work-card","attachmentId":93508996,"attachmentType":"pdf","workUrl":"https://www.academia.edu/89754980/Improving_SpellChecking_an_effective_Ad_Hoc_probabilistic_lexical_measure_for_general_typos"}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div></div></div></div><div data-auto_select="false" data-client_id="331998490334-rsn3chp12mbkiqhl6e7lu2q0mlbu0f1b" data-doc_id="93508996" data-landing_url="https://www.academia.edu/89754980/Improving_SpellChecking_an_effective_Ad_Hoc_probabilistic_lexical_measure_for_general_typos" data-login_uri="https://www.academia.edu/registrations/google_one_tap" data-moment_callback="onGoogleOneTapEvent" id="g_id_onload"></div><div class="ds-top-related-works--grid-container"><div class="ds-related-content--container ds-top-related-works--container"><h2 class="ds-related-content--heading">Related papers</h2><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="0" data-entity-id="12498175" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/12498175/Arabic_Spelling_Error_Detection_and_Correction">Arabic Spelling Error Detection and Correction</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="30994194" href="https://buid.academia.edu/KShaalan">Khaled Shaalan</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Arabic Spelling Error Detection and Correction, 2015</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Arabic Spelling Error Detection and Correction","attachmentId":37693893,"attachmentType":"pdf","work_url":"https://www.academia.edu/12498175/Arabic_Spelling_Error_Detection_and_Correction","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/12498175/Arabic_Spelling_Error_Detection_and_Correction"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="1" data-entity-id="97330276" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/97330276/Global_Spelling_Correction_in_Context_using_Language_Models_Application_to_the_Arabic_Language">Global Spelling Correction in Context using Language Models: Application to the Arabic Language</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="159228945" href="https://fsjesk.academia.edu/SAIDALAAROUSSI">SAIDA LAAROUSSI</a><span>, </span><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="260808182" href="https://independent.academia.edu/SiAouragh">Si Aouragh</a></div><p class="ds-related-work--metadata ds2-5-body-xs">International Journal of Computing and Digital Systems</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Global Spelling Correction in Context using Language Models: Application to the Arabic Language","attachmentId":98980779,"attachmentType":"pdf","work_url":"https://www.academia.edu/97330276/Global_Spelling_Correction_in_Context_using_Language_Models_Application_to_the_Arabic_Language","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/97330276/Global_Spelling_Correction_in_Context_using_Language_Models_Application_to_the_Arabic_Language"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="2" data-entity-id="2752833" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/2752833/Improved_Spelling_Error_Detection_and_Correction_for_Arabic">Improved Spelling Error Detection and Correction for Arabic</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="348759" href="https://dcu.academia.edu/MohammedAttia">Mohammed Attia</a></div><p class="ds-related-work--abstract ds2-5-body-sm">ABSTRACT A spelling error detection and correction application is based on three main components: a dictionary (or reference word list), an error model and a language model. While most of the attention in the literature has been directed to the language model, we show how improvements in any of the three components can lead to significant cumulative improvements in the overall performance of the system.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Improved Spelling Error Detection and Correction for Arabic","attachmentId":30725218,"attachmentType":"pdf","work_url":"https://www.academia.edu/2752833/Improved_Spelling_Error_Detection_and_Correction_for_Arabic","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/2752833/Improved_Spelling_Error_Detection_and_Correction_for_Arabic"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="3" data-entity-id="108065036" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/108065036/Arabic_spellchecking_a_depth_filtered_composition_metric_to_achieve_fully_automatic_correction">Arabic spellchecking: a depth-filtered composition metric to achieve fully automatic correction</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="163474776" href="https://independent.academia.edu/JournalIJECE">International Journal of Electrical and Computer Engineering (IJECE)</a></div><p class="ds-related-work--metadata ds2-5-body-xs">International Journal of Electrical and Computer Engineering (IJECE), 2023</p><p class="ds-related-work--abstract ds2-5-body-sm">Digital environments for human learning have evolved a lot in recent years thanks to incredible advances in information technologies. Computer assistance for text creation and editing tools represent a future market in which natural language processing (NLP) concepts will be used. This is particularly the case of the automatic correction of spelling mistakes used daily by data operators. Unfortunately, these spellcheckers are considered writing aids tools, they are unable to perform this task automatically without user's assistance. In this paper, we suggest a filtered composition metric based on the weighting of two lexical similarity distances in order to reach the auto-correction. The approach developed in this article requires the use of two phases: the first phase of correction involves combining two well-known distances: the edit distance weighted by relative weights of the proximity of the Arabic keyboard and the calligraphical similarity between Arabic alphabet, and combine this measure with the Jaro-Winkler distance to better weight, filter solutions having the same metric. The second phase is considered as a booster of the first phase, this use the probabilistic bigram language model after the recognition of the solutions of error, which may have the same lexical similarity measure in the first correction phase. The evaluation of the experimental results obtained from the test performed by our filtered composition measure on a dataset of errors allowed us to achieve a 96% of auto-correction rate. This is an open access article under the CC BY-SA license.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Arabic spellchecking: a depth-filtered composition metric to achieve fully automatic correction","attachmentId":106550430,"attachmentType":"pdf","work_url":"https://www.academia.edu/108065036/Arabic_spellchecking_a_depth_filtered_composition_metric_to_achieve_fully_automatic_correction","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/108065036/Arabic_spellchecking_a_depth_filtered_composition_metric_to_achieve_fully_automatic_correction"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="4" data-entity-id="118190007" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/118190007/New_Language_Models_for_Spelling_Correction">New Language Models for Spelling Correction</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="260808182" href="https://independent.academia.edu/SiAouragh">Si Aouragh</a></div><p class="ds-related-work--metadata ds2-5-body-xs">The International Arab Journal of Information Technology</p><p class="ds-related-work--abstract ds2-5-body-sm">Correcting spelling errors based on the context is a fairly significant problem in Natural Language Processing (NLP) applications. The majority of the work carried out to introduce the context into the process of spelling correction uses the n-gram language models. However, these models fail in several cases to give adequate probabilities for the suggested solutions of a misspelled word in a given context. To resolve this issue, we propose two new language models inspired by stochastic language models combined with edit distance. A first phase consists in finding the words of the lexicon orthographically close to the erroneous word and a second phase consists in ranking and limiting these suggestions. We have applied the new approach to Arabic language taking into account its specificity of having strong contextual connections between distant words in a sentence. To evaluate our approach, we have developed textual data processing applications, namely the extraction of distant transi...</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"New Language Models for Spelling Correction","attachmentId":113874481,"attachmentType":"pdf","work_url":"https://www.academia.edu/118190007/New_Language_Models_for_Spelling_Correction","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/118190007/New_Language_Models_for_Spelling_Correction"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="5" data-entity-id="87685836" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/87685836/Arabic_Spelling_Correction_using_Supervised_Learning">Arabic Spelling Correction using Supervised Learning</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="16223579" href="https://cairo.academia.edu/AmirAtiya">Amir Atiya</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Proceedings of the EMNLP 2014 Workshop on Arabic Natural Language Processing (ANLP), 2014</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Arabic Spelling Correction using Supervised Learning","attachmentId":91828043,"attachmentType":"pdf","work_url":"https://www.academia.edu/87685836/Arabic_Spelling_Correction_using_Supervised_Learning","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/87685836/Arabic_Spelling_Correction_using_Supervised_Learning"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="6" data-entity-id="108516188" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/108516188/Efficient_Weighted_Edit_Distance_and_N_gram_Language_Models_to_Improve_Spelling_Correction_of_Segmentation_Errors">Efficient Weighted Edit Distance and N-gram Language Models to Improve Spelling Correction of Segmentation Errors</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="7724111" href="https://independent.academia.edu/GueddahHicham">Gueddah Hicham</a></div><p class="ds-related-work--metadata ds2-5-body-xs">International Journal of Advanced Computer Science and Applications, 2021</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Efficient Weighted Edit Distance and N-gram Language Models to Improve Spelling Correction of Segmentation Errors","attachmentId":106877107,"attachmentType":"pdf","work_url":"https://www.academia.edu/108516188/Efficient_Weighted_Edit_Distance_and_N_gram_Language_Models_to_Improve_Spelling_Correction_of_Segmentation_Errors","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/108516188/Efficient_Weighted_Edit_Distance_and_N_gram_Language_Models_to_Improve_Spelling_Correction_of_Segmentation_Errors"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="7" data-entity-id="2687054" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/2687054/An_In_depth_Study_of_the_Automatic_Detection_and_Correction_of_Spelling_Mistakes">An In-depth Study of the Automatic Detection and Correction of Spelling Mistakes</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="35462" href="https://glasgow.academia.edu/IadhOunis">Iadh Ounis</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2005</p><p class="ds-related-work--abstract ds2-5-body-sm">ABSTRACT This paper discusses the issues involved in an information retrieval system when spelling errors are encountered in a query. We look at two classical algorithms that may be used to correct these errors and their consequent effect on the system's performance. The algorithms are the Levenshtein Distance and the Longest Common Subsequence. We experiment on a variety of test data and explore the impact of certain errors on an information retrieval system.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"An In-depth Study of the Automatic Detection and Correction of Spelling Mistakes","attachmentId":30680850,"attachmentType":"pdf","work_url":"https://www.academia.edu/2687054/An_In_depth_Study_of_the_Automatic_Detection_and_Correction_of_Spelling_Mistakes","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/2687054/An_In_depth_Study_of_the_Automatic_Detection_and_Correction_of_Spelling_Mistakes"><span 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