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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 word co-occurrence information shows potential but only improves overall accuracy slightly.","publication_date":"2011,,","publication_name":"Proceedings of the Conference on Empirical Methods in Natural Language Processing","grobid_abstract_attachment_id":"46809875"},"document_type":"paper","pre_hit_view_count_baseline":null,"quality":"high","language":"en","title":"Exploiting syntactic and distributional information for spelling correction with web-scale n-gram models","broadcastable":false,"draft":null,"has_indexable_attachment":true,"indexable":true}}["work"]; window.loswp.workCoauthors = [50446196]; 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="" 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data-author-id="26312099" href="https://independent.academia.edu/AMINULISLAM93">AMINUL ISLAM</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2009 International Conference on Natural Language Processing and Knowledge Engineering, 2009</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":"Real-word spelling correction using Google Web 1T n-gram with backoff","attachmentId":47097035,"attachmentType":"pdf","work_url":"https://www.academia.edu/10822798/Real_word_spelling_correction_using_Google_Web_1T_n_gram_with_backoff","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/10822798/Real_word_spelling_correction_using_Google_Web_1T_n_gram_with_backoff"><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="11431970" 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/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-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="27824588" href="https://independent.academia.edu/TarikulIslam29">Tarikul Islam</a></div><p class="ds-related-work--metadata ds2-5-body-xs">International Conference on Information and Knowledge Management, 2009</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":"Real-word spelling correction using Google web 1Tn-gram data set","attachmentId":46713669,"attachmentType":"pdf","work_url":"https://www.academia.edu/11431970/Real_word_spelling_correction_using_Google_web_1Tn_gram_data_set","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/11431970/Real_word_spelling_correction_using_Google_web_1Tn_gram_data_set"><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="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="3" data-entity-id="12906666" 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/12906666/Real_Word_Spelling_Correction_using_Google_Web_1T_3_grams">Real-Word Spelling Correction using Google Web 1T 3-grams</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="32068342" href="https://uottawa.academia.edu/DianaInkpen">Diana Inkpen</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Empirical Methods in Natural Language Processing, 2009</p><p class="ds-related-work--abstract ds2-5-body-sm">We present a method for detecting and correcting multiple real-word spelling er- rors using the Google Web 1T 3-gram data set and a normalized and modified ver- sion of the Longest Common Subsequence (LCS) string matching algorithm. Our method is focused mainly on how to im- prove the detection recall (the fraction of errors correctly detected) and the correc- tion</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":"Real-Word Spelling Correction using Google Web 1T 3-grams","attachmentId":37875773,"attachmentType":"pdf","work_url":"https://www.academia.edu/12906666/Real_Word_Spelling_Correction_using_Google_Web_1T_3_grams","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/12906666/Real_Word_Spelling_Correction_using_Google_Web_1T_3_grams"><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="11431942" 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/11431942/Real_word_spelling_correction_using_Google_Web_IT_3_grams">Real-word spelling correction using Google Web IT 3-grams</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="27824588" href="https://independent.academia.edu/TarikulIslam29">Tarikul Islam</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Proceedings of the 2009 Conference on …, 2009</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":"Real-word spelling correction using Google Web IT 3-grams","attachmentId":46713738,"attachmentType":"pdf","work_url":"https://www.academia.edu/11431942/Real_word_spelling_correction_using_Google_Web_IT_3_grams","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/11431942/Real_word_spelling_correction_using_Google_Web_IT_3_grams"><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="22557625" 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/22557625/Using_Grapheme_n_grams_in_Spelling_Correction_and_Augmentative_Typing_Systems">Using Grapheme n-grams in Spelling Correction and Augmentative Typing Systems</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="45137795" href="https://independent.academia.edu/ALKETMEMUSHA">ALKET MEMUSHA</a><span>, </span><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="42935179" href="https://ltu.academia.edu/TarekSobh">Tarek Sobh</a></div><p class="ds-related-work--abstract ds2-5-body-sm">Probabilistic language models have gained popularity in Natural Language Processing due to their ability to successfully capture language structures and constraints with computational efficiency. Probabilistic language models are flexible and easily adapted to language changes over time as well as to some new languages. Probabilistic language models can be trained and their accuracy strongly related to the availability of large text corpora. In this paper, we investigate the usability of grapheme probabilistic models, specifically grapheme n-grams models in spellchecking as well as augmentative typing systems. Grapheme n-gram models require substantially smaller training corpora and that is one of the main drivers for this thesis in which we build grapheme n-gram language models for the Albanian language. There are presently no available Albanian language corpora to be used for probabilistic language modeling. Our technique attempts to augment spellchecking and typing systems by utilizing grapheme n-gram language models in improving suggestion accuracy in spellchecking and augmentative typing systems. Our technique can be implemented in a standalone tool or incorporated in another tool to offer additional selection/scoring criteria.</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":"Using Grapheme n-grams in Spelling Correction and Augmentative Typing Systems","attachmentId":43627582,"attachmentType":"pdf","work_url":"https://www.academia.edu/22557625/Using_Grapheme_n_grams_in_Spelling_Correction_and_Augmentative_Typing_Systems","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/22557625/Using_Grapheme_n_grams_in_Spelling_Correction_and_Augmentative_Typing_Systems"><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="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" 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