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249:, for faster action; he made the first spelling corrector by searching the word list for plausible correct spellings that differ by a single letter or adjacent letter transpositions and presenting them to the user. Gorin made SPELL publicly accessible, as was done with most SAIL (Stanford Artificial Intelligence Laboratory) programs, and it soon spread around the world via the new ARPAnet, about ten years before personal computers came into general use. SPELL, its algorithms and data structures inspired the Unix
245:, who headed the research on this budding technology, saw it necessary to include the first spell checker that accessed a list of 10,000 acceptable words. Ralph Gorin, a graduate student under Earnest at the time, created the first true spelling checker program written as an applications program (rather than research) for general English text: SPELL for the DEC PDP-10 at Stanford University's Artificial Intelligence Laboratory, in February 1971. Gorin wrote SPELL in
139:
492:'s short-lived CoAuthor and allowed a user to view the results after a document was processed and correct only the words that were known to be wrong. When memory and processing power became abundant, spell checking was performed in the background in an interactive way, such as has been the case with the Sector Software produced Spellbound program released in 1987 and
448:
It might seem logical that where spell-checking dictionaries are concerned, "the bigger, the better," so that correct words are not marked as incorrect. In practice, however, an optimal size for
English appears to be around 90,000 entries. If there are more than this, incorrectly spelled words may be
211:, to recognize errors instead of correctly-spelled words. This approach usually requires a lot of effort to obtain sufficient statistical information. Key advantages include needing less runtime storage and the ability to correct errors in words that are not included in a dictionary.
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into new combinations of words. In German, compound nouns are frequently coined from other existing nouns. Some scripts do not clearly separate one word from another, requiring word-splitting algorithms. Each of these presents unique challenges to non-English language spell checkers.
593:-based spelling correction algorithm", published in 1999, which is able to recognize about 96% of context-sensitive spelling errors, in addition to ordinary non-word spelling errors. Context-sensitive spell checkers appeared in the now-defunct applications
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but it was not so helpful for logical or phonetic errors. The challenge the developers faced was the difficulty in offering useful suggestions for misspelled words. This requires reducing words to a skeletal form and applying pattern-matching algorithms.
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The first MS-DOS spell checkers were mostly used in proofing mode from within word processing packages. After preparing a document, a user scanned the text looking for misspellings. Later, however, batch processing was offered in such packages as
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The original version of this poem was written by
Jerrold H. Zar in 1992. An unsophisticated spell checker will find little or no fault with this poem because it checks words in isolation. A more sophisticated spell checker will make use of a
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than a reference to the Thai currency. Hence, it would typically be more useful if a few people who write about Thai currency were slightly inconvenienced than if the spelling errors of the many more people who discuss baths were overlooked.
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It then compares each word with a known list of correctly spelled words (i.e. a dictionary). This might contain just a list of words, or it might also contain additional information, such as hyphenation points or lexical and grammatical
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The first spell checkers for personal computers appeared in 1980, such as "WordCheck" for
Commodore systems which was released in late 1980 in time for advertisements to go to print in January 1981. Developers such as Maria Mariani and
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of the surrounding words. Not only does this allow words such as those in the poem above to be caught, but it mitigates the detrimental effect of enlarging dictionaries, allowing more words to be recognized. For example,
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English is unusual in that most words used in formal writing have a single spelling that can be found in a typical dictionary, with the exception of some jargon and modified words. In many languages, words are often
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When Apple developed "a system-wide spelling checker" for Mac OS X so that "the operating system took over spelling fixes," it was a first: one "didn't have to maintain a separate spelling checker for each" program.
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might not have justified the investment of implementing a spell checker, companies like WordPerfect nonetheless strove to localize their software for as many national markets as possible as part of their global
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It is unclear whether morphological analysis—allowing for many forms of a word depending on its grammatical role—provides a significant benefit for
English, though its benefits for highly
396:, introduced in 1994, was "designed for developers of applications that support Windows." It came with a dictionary but had the ability to build and incorporate use of secondary dictionaries.
314:. Its goal is to combine programs supporting different languages such as Aspell, Hunspell, Nuspell, Hspell (Hebrew), Voikko (Finnish), Zemberek (Turkish) and AppleSpell under one interface.
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packages or end-user products into the rapidly expanding software market. On the pre-Windows PCs, these spell checkers were standalone programs, many of which could be run in
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850:, citation: "Maria Mariani... was one of a group of six linguists from Georgetown University who developed the first spell-check system for the IBM corporation."
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There has been research on developing algorithms that are capable of recognizing a misspelled word, even if the word itself is in the vocabulary, based on the
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181:. For many other languages, such as those featuring agglutination and more complex declension and conjugation, this part of the process is more complicated.
1536:
728:
Proceedings of the 9th
International Conference on Natural Language Processing (PolTAL). Lecture Notes in Computer Science (LNCS). Springer. p. 438-449.
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Advances in Data Mining: Applications and
Theoretical Aspects: 10th Industrial Conference, ICDM 2010, Berlin, Germany, July 12-14, 2010. Proceedings
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The first spell checkers were "verifiers" instead of "correctors." They offered no suggestions for incorrectly spelled words. This was helpful for
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However, the market for standalone packages was short-lived, as by the mid-1980s developers of popular word-processing packages like
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had incorporated spell checkers in their packages, mostly licensed from the above companies, who quickly expanded support from just
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Due to the inability of traditional spell checkers to check words in complex inflected languages, Hungarian László Németh developed
358:. However, this required increasing sophistication in the morphology routines of the software, particularly with regard to heavily-
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to find correct spellings of misspelled words. An alternative type of spell checker uses solely statistical information, such as
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The first spell checkers were widely available on mainframe computers in the late 1970s. A group of six linguists from
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285:. Aspell's main improvement is that it can more accurately suggest correct alternatives for misspelled English words.
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for those misspellings; this less flexible approach is often used in paper-based correction methods, such as the
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that non-native language learners can rely on to detect and correct their misspellings in the target language.
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program commonly used in Unix is based on R. E. Gorin's SPELL. It was converted to C by Pace
Willisson at MIT.
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and complex compound words. Hunspell also uses
Unicode in its dictionaries. Hunspell replaced the previous
173:, the spell checker will need to consider different forms of the same word, such as plurals, verbal forms,
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Between Sound and
Spelling: Combining Phonetics and Clustering Algorithms to Improve Target Word Recovery.
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Proceedings of Recent
Advances in Natural Language Processing (RANLP2013). Hissar, Bulgaria. p. 172-178.
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skipped because they are mistaken for others. For example, a linguist might determine on the basis of
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Some spell checkers have separate support for medical dictionaries to help prevent medical errors.
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attempt to fix problems with grammar beyond spelling errors, including incorrect choice of words.
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and foreign words as misspellings. Nonetheless, spell checkers can be considered as a type of
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spell checker in action for the above poem, the word "chequer" marked as unrecognized word
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1212:. (pp. 29). Master's Thesis, Dominican University of California. Retrieved 19 March 2012.
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errors. However, even at their best, they rarely catch all the errors in a text (such as
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835:"Georgetown U Faculty & Staff: The Center for Language, Education & Development"
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History and text of "Candidate for a Pullet Surprise" by Mark Eckman and Jerrold H. Zar
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387:'s spellcheck coverage includes virtually all bundled and third party applications.
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allows users to approve or reject replacements and modify the program's operation.
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Spell checkers became increasingly sophisticated; now capable of recognizing
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have also been used for spell checking combined with phonetic information.
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mode from within word-processing packages on PCs with sufficient memory.
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The most successful algorithm to date is Andrew Golding and Dan Roth's "
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invented one for the VAX machines of Digital Equipment Corp in 1981.
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1100:
Peter G. Aitken (November 8, 1994). "Spell-Checking for your Apps".
370:. Although the size of the word-processing market in a country like
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In some cases, spell checkers use a fixed list of misspellings and
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An additional step is a language-dependent algorithm for handling
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560:. The most common example of errors caught by such a system are
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developed the first spell-check system for the IBM corporation.
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1184:"CASES; Do Spelling and Penmanship Count? In Medicine, You Bet"
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Method for rule-based correction of spelling and grammar errors
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Foreign Language Learning Difficulties and Teaching Strategies
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Computer Programs for Detecting and Correcting Spelling Errors
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Effective Spell Checking Methods Using Clustering Algorithms.
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1346:, Spell-Check Crutch Curtails Correctness, by Lloyd de Vries
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A basic spell checker carries out the following processes:
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errors, such as the bold words in the following sentence:
1901:
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Golding, Andrew R.; Roth, Dan (1999). "Journal Article".
862:"Teaching Computers to Spell (obituary for Henry Kučera)"
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It scans the text and extracts the words contained in it.
903:
829:
827:
1136:"Medical Spell Checker for Firefox and Thunderbird"
1118:"Aspell and Hunspell: A Tale of Two Spell Checkers"
532:
416:. Prior to using Hunspell, Firefox and Chrome used
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192:As an adjunct to these components, the program's
189:such as German, Hungarian, or Turkish are clear.
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54:. Spell-checking features are often embedded in
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519:Spell-checking for languages other than English
310:is another general spell checker, derived from
133:to consider the context in which a word occurs.
1340:, "Spellchecking by computer", by Roger Mitton
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169:. Even for a lightly inflected language like
556:would not be recognized as a misspelling of
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1331:, "How to Write a Spelling Corrector", by
1295:"Google's Context-Sensitive Spell Checker"
1181:
1159:
1026:Mac OS X Snow Leopard: The Missing Manual
982:Compute! Magazine, Issue 8, Vol. 3, No. 1
860:Harvey, Charlotte Bruce (May–June 2010).
672:. Springer Science & Business Media.
112:It helps me right all stiles of righting,
1283:. googlesystem.blogspot.com. 29 May 2009
1042:Switching to the Mac: The Missing Manual
988:
770:
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90:Eye strike the quays and type a whirred
27:Software to help correct spelling errors
14:
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281:The GNU project has its spell checker
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745:. Stanford University. Archived from
724:Zampieri, M.; de Amorim, R.C. (2014)
704:de Amorim, R.C.; Zampieri, M. (2013)
117:Each frays come posed up on my screen
1836:Simple Knowledge Organization System
459:is more frequently a misspelling of
412:offer spell checking support, using
1182:Friedman, Richard A.; D, M (2003).
796:
740:"The First Three Spelling Checkers"
737:
24:
1138:. e-MedTools. 2017. Archived from
50:that checks for misspellings in a
25:
2152:
1851:Thesaurus (information retrieval)
1313:
1162:"German medical dictionary words"
771:Peterson, James (December 1980).
121:The chequer pours o'er every word
1257:Walt Mossberg (4 January 2007).
1072:. February 21, 1994. p. 68.
975:"Micro Computer Industries, Ltd"
533:Context-sensitive spell checkers
435:
292:, a spell checker that supports
85:It plane lee marks four my revue
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995:Advertisement (November 1982).
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103:Its vary polished in its weigh.
94:Weather eye am write oar wrong
1432:Natural language understanding
1160:Quathamer, Dr. Tobias (2016).
973:Advertisement (January 1981).
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123:Two cheque sum spelling rule.
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1:
1956:Optical character recognition
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110:It freeze yew lodes of thyme.
96:It tells me straight a weigh.
1649:Multi-document summarization
666:Perner, Petra (2010-07-05).
513:foreign language writing aid
119:Eye trussed too bee a joule.
101:Your shore real glad two no.
81:Eye have a spelling chequer,
7:
2141:Natural language processing
1979:Latent Dirichlet allocation
1951:Natural language generation
1816:Machine-readable dictionary
1811:Linguistic Linked Open Data
1386:Natural language processing
1084:"Browse September 27, 1993"
610:
399:
333:terminate-and-stay-resident
201:approximate string matching
114:And aides me when eye rime.
108:A chequer is a bless thing,
99:Eye ran this poem threw it,
92:And weight four it two say
87:Miss Steaks I can knot sea.
10:
2157:
1731:Explicit semantic analysis
1480:Deep linguistic processing
1066:"VisualTools VT-Speller".
1045:. "O'Reilly Media, Inc.".
997:"The Spelling Bee Is Over"
231:
222:entries of encyclopedias.
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1992:
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1574:Word-sense disambiguation
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1427:Computational linguistics
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1281:"Google Operating System"
1230:. SpringerLink: 107–130.
548:in the same paragraph as
236:
149:
105:My chequer tolled me sew.
2100:Natural Language Toolkit
2024:Pronunciation assessment
1926:Automatic identification
1756:Latent semantic analysis
1712:Distributional semantics
1597:Compound-term processing
1495:Named-entity recognition
800:Visible Legacies for Y3K
83:It came with my Pea Sea.
2004:Automated essay scoring
1974:Document classification
1641:Automatic summarization
1236:10.1023/A:1007545901558
294:agglutinative languages
269:
199:Spell checkers can use
58:or services, such as a
1861:Universal Dependencies
1554:Terminology extraction
1537:Semantic decomposition
1532:Semantic role labeling
1522:Part-of-speech tagging
1490:Information extraction
1475:Coreference resolution
1465:Collocation extraction
1320:List of spell checkers
1164:. Dr. Tobias Quathamer
880:"International Ispell"
627:Record linkage problem
507:errors) and will flag
484:
317:
146:
1622:Sentence segmentation
1261:. Wall Street Journal
956:, AbiWord, 2023-02-13
866:Brown Alumni Magazine
692:U.S. Patent 6618697,
595:Microsoft Office 2007
474:
404:Web browsers such as
258:Georgetown University
226:Clustering algorithms
141:
2131:Text editor features
2074:Voice user interface
1785:datasets and corpora
1726:Document-term matrix
1579:Word-sense induction
1039:David Pogue (2015).
1024:David Pogue (2009).
354:and eventually even
276:International Ispell
205:Levenshtein distance
2054:Interactive fiction
1984:Pachinko allocation
1941:Speech segmentation
1897:Google Ngram Viewer
1669:Machine translation
1659:Text simplification
1654:Sentence extraction
1542:Semantic similarity
632:Spelling suggestion
203:algorithms such as
187:synthetic languages
2064:Question answering
1936:Speech recognition
1801:Corpus linguistics
1781:Language resources
1564:Textual entailment
1547:Sentiment analysis
1206:Banks, T. (2008).
1188:The New York Times
932:hunspell.github.io
752:on 22 October 2012
711:2017-08-17 at the
485:
451:corpus linguistics
304:in version 2.0.2.
147:
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2112:
2069:Virtual assistant
1994:Computer-assisted
1920:
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1677:Computer-assisted
1635:
1634:
1627:Word segmentation
1589:Text segmentation
1527:Semantic analysis
1515:Syntactic parsing
1500:Ontology learning
1122:battlepenguin.com
928:"Hunspell: About"
679:978-3-642-14399-1
247:assembly language
16:(Redirected from
2148:
2090:Formal semantics
2039:Natural language
1946:Speech synthesis
1928:and data capture
1831:Semantic network
1806:Lexical resource
1789:
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1607:Lexical analysis
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1510:Semantic parsing
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1224:Machine Learning
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475:A screenshot of
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48:software feature
40:spelling checker
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2014:Grammar checker
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2009:Concordancer
1405:Bag-of-words
1333:Peter Norvig
1301:25 September
1299:. Retrieved
1287:25 September
1285:. Retrieved
1275:
1265:24 September
1263:. Retrieved
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907:
904:"GNU Aspell"
898:
887:. Retrieved
883:
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839:the original
815:. Retrieved
808:the original
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747:the original
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647:LanguageTool
637:Words (Unix)
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175:contractions
153:
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64:email client
43:
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35:
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1966:Topic model
1846:Text corpus
1692:Statistical
1559:Text mining
1400:AI-complete
1344:CBSNews.com
1103:PC Magazine
1002:PC Magazine
599:Google Wave
501:grammatical
428:Specialties
344:WordPerfect
243:Les Earnest
216:suggestions
179:possessives
162:attributes.
44:spell check
2120:Categories
1687:Rule-based
1569:Truecasing
1437:Stop words
1329:Norvig.com
1193:2018-08-29
1168:2018-08-29
1146:2018-08-29
1088:VT-SPELLER
1009:21 October
960:2023-02-19
937:2023-02-19
913:2023-02-19
908:aspell.net
889:2023-02-19
845:2008-12-18
817:2011-02-18
783:2011-02-18
756:10 October
653:References
509:neologisms
422:GNU Aspell
394:VT Speller
379:strategy.
283:GNU Aspell
167:morphology
68:dictionary
1996:reviewing
1794:standards
1792:Types and
1338:BBK.ac.uk
562:homophone
505:homophone
377:marketing
364:Hungarian
253:program.
241:In 1961,
2136:Spelling
1912:Wikidata
1892:FrameNet
1877:BabelNet
1856:Treebank
1826:PropBank
1771:Word2vec
1736:fastText
1617:Stemming
1259:"Review"
1244:12283016
709:Archived
611:See also
554:Thailand
414:Hunspell
400:Browsers
385:Mac OS X
352:European
350:to many
340:WordStar
290:Hunspell
220:see also
56:software
32:software
2083:Related
2049:Chatbot
1907:WordNet
1887:DBpedia
1761:Seq2seq
1505:Parsing
1420:Trigram
571:coming
539:context
481:AbiWord
477:Enchant
418:MySpell
406:Firefox
372:Iceland
368:Finnish
348:English
327:rushed
312:AbiWord
308:Enchant
298:MySpell
232:History
209:n-grams
171:English
46:) is a
2056:(c.f.
1714:models
1702:Neural
1415:Bigram
1410:n-gram
1324:Curlie
1242:
1049:
676:
591:Winnow
490:Oracle
479:, the
251:ispell
237:Pre-PC
177:, and
150:Design
2105:spaCy
1750:large
1741:GloVe
1240:S2CID
978:(PDF)
811:(PDF)
804:(PDF)
778:(PDF)
750:(PDF)
743:(PDF)
569:Their
442:typos
70:, or
1870:Data
1721:BERT
1303:2010
1289:2010
1267:2010
1047:ISBN
1011:2013
758:2011
674:ISBN
597:and
583:reel
558:bath
550:Thai
545:baht
461:bath
456:baht
420:and
408:and
366:and
342:and
274:The
270:Unix
52:text
38:(or
34:, a
1902:UBY
1322:at
1232:doi
580:its
578:if
576:sea
573:too
552:or
465:bat
463:or
329:OEM
318:PCs
300:in
42:or
30:In
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20:)
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