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(PDF) Exploring distributional similarity based models for query spelling correction
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This paper describes novel methods for use of distributional similarity estimated from query logs in learning improved query spelling correction models. The key to our methods is the property of distributional similarity between two terms: it is high between a frequently occurring misspelling and its correction, and low between two irrelevant terms only with similar spellings. We present two models that are able to take advantage of this property. 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const worksViewsPath = "/v0/works/views?subdomain_param=api&work_ids%5B%5D=59965783"; const getWorkViews = async (workId) => { const response = await fetch(worksViewsPath); if (!response.ok) { throw new Error('Failed to load work views'); } const data = await response.json(); return data.views[workId]; }; // Get the view count for the work - we send this immediately rather than waiting for // the DOM to load, so it can be available as soon as possible (but without holding up // the backend or other resource requests, because it's a bit expensive and not critical). const viewCount = await getWorkViews(workId); const updateViewCount = (viewCount) => { try { const viewCountNumber = parseInt(viewCount, 10); if (viewCountNumber === 0) { // Remove the whole views element if there are zero views. document.getElementById('work-metadata-view-count')?.parentNode?.remove(); return; } const commaizedViewCount = viewCountNumber.toLocaleString(); const viewCountBody = document.getElementById('work-metadata-view-count'); if (!viewCountBody) { throw new Error('Failed to find work views element'); } viewCountBody.textContent = `${commaizedViewCount} views`; } catch (error) { // Remove the whole views element if there was some issue parsing. document.getElementById('work-metadata-view-count')?.parentNode?.remove(); throw new Error(`Failed to parse view count: ${viewCount}`, error); } }; // If the DOM is still loading, wait for it to be ready before updating the view count. if (document.readyState === "loading") { document.addEventListener('DOMContentLoaded', () => { updateViewCount(viewCount); }); // Otherwise, just update it immediately. } else { updateViewCount(viewCount); } })();</script></div><p class="ds-work-card--work-abstract ds-work-card--detail ds2-5-body-md">A query speller is crucial to search engine in improving web search relevance. This paper describes novel methods for use of distributional similarity estimated from query logs in learning improved query spelling correction models. The key to our methods is the property of distributional similarity between two terms: it is high between a frequently occurring misspelling and its correction, and low between two irrelevant terms only with similar spellings. We present two models that are able to take advantage of this property. Experimental results demonstrate that the distributional similarity based models can significantly outperform their baseline systems in the web query spelling correction task.</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":73622956,"attachmentType":"pdf","workUrl":"https://www.academia.edu/59965783/Exploring_distributional_similarity_based_models_for_query_spelling_correction"}">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":73622956,"attachmentType":"pdf","workUrl":"https://www.academia.edu/59965783/Exploring_distributional_similarity_based_models_for_query_spelling_correction"}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div><div class="ds-signup-banner-trigger-container"><div class="ds-signup-banner-trigger ds-signup-banner-trigger-premium-marketing"></div></div><div class="ds-signup-banner ds-signup-banner-premium-marketing"><div id="ds-signup-banner-close-button"><button class="ds2-5-button ds2-5-button--secondary ds2-5-button--inverse"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">close</span></button></div><div class="premium-banner-content"><div class="left"><img src="//a.academia-assets.com/images/academia-logo-capital-white.svg" /><span>Get access to the world's latest research</span></div><div class="right"><div class="card free"><div class="header">Free</div><div class="feature-list"><div class="feature"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">check</span><span>Download one paper at a time</span></div><div class="feature"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">check</span><span>Save papers to bookmarks</span></div><div class="feature"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">check</span><span>Basic search</span></div></div><button class="ds2-5-button ds2-5-button--secondary ds2-5-button--small ds2-5-button--inverse ds2-5-button--full-width js-swp-download-button" data-signup-modal="{"location":"premium-banner-desktop-free"}">Sign up for free</button></div><div class="card premium"><div class="pill">Recommended</div><div class="header premium">Premium</div><div class="feature-list"><div class="feature"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">check</span><span>Get highly curated PDF packages</span></div><div class="feature"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">check</span><span>Track your impact with Mentions</span></div><div class="feature"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">check</span><span>Access advanced search filters</span></div><div class="feature"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">check</span><span>Support Academia’s mission</span></div><div class="feature"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">check</span><span>Create your personal website</span></div></div><button class="ds2-5-button ds2-5-button--small ds2-5-button--inverse ds2-5-button--full-width js-swp-download-button" data-signup-modal="{"location":"premium-banner-desktop-upgrade","submitText":"Try Premium for $1"}">Try Premium for $1</button></div></div></div></div><script>(() => { // Set up signup banner show/hide behavior: // 1. 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Typically, the error model relies on a weighted string edit distance measure. The weights can be learned from pairs of misspelled words and their corrections. This paper investigates using the Expectation Maximization algorithm to learn edit distance weights directly from search query logs, without relying on a corpus of paired words.</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":"Learning a Spelling Error Model from Search Query Logs","attachmentId":33444293,"attachmentType":"pdf","work_url":"https://www.academia.edu/6725503/Learning_a_Spelling_Error_Model_from_Search_Query_Logs","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/6725503/Learning_a_Spelling_Error_Model_from_Search_Query_Logs"><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="101689416" 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/101689416/A_large_scale_ranker_based_system_for_search_query_spelling_correction">A large scale ranker-based system for search query 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="269471175" href="https://independent.academia.edu/MicolDaniel">Daniel Micol</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2010</p><p class="ds-related-work--abstract ds2-5-body-sm">This paper makes three significant extensions to a noisy channel speller designed for standard written text to target the challenging domain of search queries. First, the noisy channel model is subsumed by a more general ranker, which allows a variety of features to be easily incorporated. Second, a distributed infrastructure is proposed for training and applying Web scale n-gram language models. Third, a new phrase-based error model is presented. This model places a probability distribution over transformations between multi-word phrases, and is estimated using large amounts of query-correction pairs derived from search logs. Experiments show that each of these extensions leads to significant improvements over the stateof-the-art baseline methods.</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":"A large scale ranker-based system for search query spelling correction","attachmentId":102160938,"attachmentType":"pdf","work_url":"https://www.academia.edu/101689416/A_large_scale_ranker_based_system_for_search_query_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/101689416/A_large_scale_ranker_based_system_for_search_query_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="2" data-entity-id="101689417" 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/101689417/Learning_phrase_based_spelling_error_models_from_clickthrough_data">Learning phrase-based spelling error models from clickthrough data</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="269471175" href="https://independent.academia.edu/MicolDaniel">Daniel Micol</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2010</p><p class="ds-related-work--abstract ds2-5-body-sm">This paper explores the use of clickthrough data for query spelling correction. First, large amounts of query-correction pairs are derived by analyzing users' query reformulation behavior encoded in the clickthrough data. Then, a phrase-based error model that accounts for the transformation probability between multi-term phrases is trained and integrated into a query speller system. Experiments are carried out on a human-labeled data set. Results show that the system using the phrase-based error model outperforms significantly its baseline systems. 3 Clickthrough Data and Spelling Correction This section describes the way the query-correction pairs are extracted from click</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":"Learning phrase-based spelling error models from clickthrough data","attachmentId":102160936,"attachmentType":"pdf","work_url":"https://www.academia.edu/101689417/Learning_phrase_based_spelling_error_models_from_clickthrough_data","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/101689417/Learning_phrase_based_spelling_error_models_from_clickthrough_data"><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="2661513" 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/2661513/A_Discriminative_Model_for_Query_Spelling_Correction_with_Latent_Structural_SVM">A Discriminative Model for Query Spelling Correction with Latent Structural SVM</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="9644" href="https://illinois.academia.edu/DRoth">Dan Roth</a></div><p class="ds-related-work--abstract ds2-5-body-sm">Abstract Discriminative training in query spelling correction is difficult due to the complex internal structures of the data. Recent work on query spelling correction suggests a two stage approach a noisy channel model that is used to retrieve a number of candidate corrections, followed by discriminatively trained ranker applied to these candidates. The ranker, however, suffers from the fact the low recall of the first, suboptimal, search stage.</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":"A Discriminative Model for Query Spelling Correction with Latent Structural SVM","attachmentId":30660895,"attachmentType":"pdf","work_url":"https://www.academia.edu/2661513/A_Discriminative_Model_for_Query_Spelling_Correction_with_Latent_Structural_SVM","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/2661513/A_Discriminative_Model_for_Query_Spelling_Correction_with_Latent_Structural_SVM"><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="117293747" 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/117293747/Search_Query_Spell_Correction_with_Weak_Supervision_in_E_commerce">Search Query Spell Correction with Weak Supervision in E-commerce</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="9944263" href="https://independent.academia.edu/MadhuraPande">Madhura Pande</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 5: Industry Track)</p><p class="ds-related-work--abstract ds2-5-body-sm">Misspelled search queries in e-commerce can lead to empty or irrelevant products. Besides inadvertent typing mistakes, most spell mistakes occur because the user does not know the correct spelling, hence typing it as it is pronounced colloquially. This colloquial typing creates countless misspelling patterns for a single correct query. In this paper, we first systematically analyze and group different spell errors into error classes and then leverage the stateof-the-art Transformer model for contextual spell correction. We overcome the constraint of limited human labelled data by proposing novel synthetic data generation techniques for voluminous generation of training pairs needed by data hungry Transformers, without any human intervention. We further utilize weakly supervised data coupled with curriculum learning strategies to improve on tough spell mistakes without regressing on the easier ones. We show significant improvements from our model on human labeled data and online A/B experiments against multiple state-of-art models.</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":"Search Query Spell Correction with Weak Supervision in E-commerce","attachmentId":113188679,"attachmentType":"pdf","work_url":"https://www.academia.edu/117293747/Search_Query_Spell_Correction_with_Weak_Supervision_in_E_commerce","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/117293747/Search_Query_Spell_Correction_with_Weak_Supervision_in_E_commerce"><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="6426942" 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/6426942/Spelling_Correction_as_an_Iterative_Process_that_Exploits_the_Collective_Knowledge_of_Web_Users">Spelling Correction as an Iterative Process that Exploits the Collective Knowledge of Web Users</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="10129560" href="https://tudelft.academia.edu/ionsilviu">ion silviu</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2004</p><p class="ds-related-work--abstract ds2-5-body-sm">Logs of user queries to an internet search engine provide a large amount of implicit and explicit information about language. In this paper, we investigate their use in spelling correction of search queries, a task which poses many additional challenges beyond the traditional spelling correction problem. We present an approach that uses an iterative transformation of the input query strings into other strings that correspond to more and more likely queries according to statistics extracted from internet search query logs.</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":"Spelling Correction as an Iterative Process that Exploits the Collective Knowledge of Web Users","attachmentId":33228639,"attachmentType":"pdf","work_url":"https://www.academia.edu/6426942/Spelling_Correction_as_an_Iterative_Process_that_Exploits_the_Collective_Knowledge_of_Web_Users","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/6426942/Spelling_Correction_as_an_Iterative_Process_that_Exploits_the_Collective_Knowledge_of_Web_Users"><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="26513431" 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/26513431/Exploiting_syntactic_and_distributional_information_for_spelling_correction_with_web_scale_n_gram_models">Exploiting syntactic and distributional information for spelling correction with web-scale n-gram models</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="50446196" href="https://independent.academia.edu/JoelTetreault">Joel Tetreault</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Proceedings of the …, 2011</p><p class="ds-related-work--abstract ds2-5-body-sm">We propose a novel way of incorporating dependency parse and word co-occurrence information into a state-of-the-art web-scale n-gram model for spelling correction. The syntactic and distributional information provides extra evidence in addition to that provided by a web-scale n-gram corpus and especially helps with data sparsity problems. Experimental results show that introducing syntactic features into n-gram based models significantly reduces errors by up to 12.4% over the current state-of-the-art. The ...</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":"Exploiting syntactic and distributional information for spelling correction with web-scale n-gram models","attachmentId":46809804,"attachmentType":"pdf","work_url":"https://www.academia.edu/26513431/Exploiting_syntactic_and_distributional_information_for_spelling_correction_with_web_scale_n_gram_models","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/26513431/Exploiting_syntactic_and_distributional_information_for_spelling_correction_with_web_scale_n_gram_models"><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="2597331" 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/2597331/Spelling_correction_for_search_engine_queries">Spelling correction for search engine queries</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="390632" href="https://lisboa.academia.edu/BrunoMartins">Bruno Martins</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2004</p><p class="ds-related-work--abstract ds2-5-body-sm">Search engines have become the primary means of accessing information on the Web. However, recent studies show misspelled words are very common in queries to these systems. When users misspell query, the results are incorrect or provide inconclusive information. In this work, we discuss the integration of a spelling correction component into tumba!, our community Web search engine.</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":"Spelling correction for search engine queries","attachmentId":30610311,"attachmentType":"pdf","work_url":"https://www.academia.edu/2597331/Spelling_correction_for_search_engine_queries","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/2597331/Spelling_correction_for_search_engine_queries"><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="8" 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="9" data-entity-id="40139377" 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/40139377/A_Benchmark_Corpus_of_English_Misspellings_and_a_Minimally_supervised_Model_for_Spelling_Correction">A Benchmark Corpus of English Misspellings and a Minimally-supervised Model 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="3199073" href="https://ets.academia.edu/MichaelFlor">Michael Flor</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications, 2019</p><p class="ds-related-work--abstract ds2-5-body-sm">Spelling correction has attracted a lot of attention in the NLP community. However, models have been usually evaluated on artificially-created or proprietary corpora. A publicly-available corpus of authentic misspellings, annotated in context, is still lacking. To address this, we present and release an annotated data set of 6,121 spelling errors in context, based on a corpus of essays written by English language learners. We also develop a minimally-supervised context-aware approach to spelling correction. It achieves strong results on our data: 88.12% accuracy. This approach can also train with a minimal amount of annotated data (performance reduced by less than 1%). Furthermore, this approach allows easy porta-bility to new domains. We evaluate our model on data from a medical domain and demonstrate that it rivals the performance of a model trained and tuned on in-domain data.</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":"A Benchmark Corpus of English Misspellings and a Minimally-supervised Model for Spelling Correction","attachmentId":60355773,"attachmentType":"pdf","work_url":"https://www.academia.edu/40139377/A_Benchmark_Corpus_of_English_Misspellings_and_a_Minimally_supervised_Model_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/40139377/A_Benchmark_Corpus_of_English_Misspellings_and_a_Minimally_supervised_Model_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></div><div class="ds-sticky-ctas--wrapper js-loswp-sticky-ctas hidden"><div class="ds-sticky-ctas--grid-container"><div class="ds-sticky-ctas--container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--sticky-ctas","attachmentId":73622956,"attachmentType":"pdf","workUrl":null}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--sticky-ctas","attachmentId":73622956,"attachmentType":"pdf","workUrl":null}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div></div></div><div class="ds-below-fold--grid-container"><div class="ds-work--container js-loswp-embedded-document"><div class="attachment_preview" data-attachment="Attachment_73622956" style="display: none"><div class="js-scribd-document-container"><div class="scribd--document-loading js-scribd-document-loader" style="display: block;"><img alt="Loading..." src="//a.academia-assets.com/images/loaders/paper-load.gif" /><p>Loading Preview</p></div></div><div style="text-align: center;"><div class="scribd--no-preview-alert js-preview-unavailable"><p>Sorry, preview is currently unavailable. You can download the paper by clicking the button above.</p></div></div></div></div><div class="ds-sidebar--container js-work-sidebar"><div class="ds-related-content--container"><h2 class="ds-related-content--heading">Related papers</h2><div class="ds-related-work--container js-related-work-sidebar-card" data-collection-position="0" data-entity-id="91486397" data-sort-order="default"><a class="ds-related-work--title js-related-work-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/91486397/An_information_retrieval_approach_to_spelling_suggestion">An information retrieval approach to spelling suggestion</a><div class="ds-related-work--metadata"><a class="js-related-work-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="246949223" href="https://independent.academia.edu/SaiKrishna1704">Sai Krishna</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Proceedings of the 19th international conference on World wide web - WWW '10, 2010</p><div 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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-related-work-grid-card-view-pdf" href="https://www.academia.edu/75300300/Spell_checking_queries_by_combining_Levenshtein_and_Stoilos_distances"><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-related-work-sidebar-card" data-collection-position="7" data-entity-id="11431970" data-sort-order="default"><a class="ds-related-work--title js-related-work-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/11431970/Real_word_spelling_correction_using_Google_web_1Tn_gram_data_set">Real-word spelling correction using Google web 1Tn-gram data set</a><div class="ds-related-work--metadata"><a class="js-related-work-grid-card-author ds2-5-body-sm ds2-5-body-link" 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