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238:, 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
234:, 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
128:
481:'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
437:
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
200:, 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.
517:
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.
582:-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.
714:
476:
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
311:
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
174:
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
385:, 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.
303:. 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
460:
839:, 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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170:. For many other languages, such as those featuring agglutination and more complex declension and conjugation, this part of the process is more complicated.
1542:
717:
Proceedings of the 9th
International Conference on Natural Language Processing (PolTAL). Lecture Notes in Computer Science (LNCS). Springer. p. 438-449.
658:
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
347:. 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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274:. 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
162:, 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.
1999:
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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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1201:. (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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824:"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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376:'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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Peter G. Aitken (November 8, 1994). "Spell-Checking for your Apps".
359:. 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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549:. 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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1173:"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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1352:, 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:
1907:
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Golding, Andrew R.; Roth, Dan (1999). "Journal Article".
851:"Teaching Computers to Spell (obituary for Henry Kučera)"
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It scans the text and extracts the words contained in it.
892:
818:
816:
1125:"Medical Spell Checker for Firefox and Thunderbird"
1107:"Aspell and Hunspell: A Tale of Two Spell Checkers"
521:
405:. Prior to using Hunspell, Firefox and Chrome used
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181:As an adjunct to these components, the program's
178:such as German, Hungarian, or Turkish are clear.
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43:. Spell-checking features are often embedded in
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508:Spell-checking for languages other than English
299:is another general spell checker, derived from
122:to consider the context in which a word occurs.
1346:, "Spellchecking by computer", by Roger Mitton
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158:. Even for a lightly inflected language like
545:would not be recognized as a misspelling of
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1027:
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1369:
1337:, "How to Write a Spelling Corrector", by
1284:"Google's Context-Sensitive Spell Checker"
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1148:
1015:Mac OS X Snow Leopard: The Missing Manual
971:Compute! Magazine, Issue 8, Vol. 3, No. 1
849:Harvey, Charlotte Bruce (May–June 2010).
661:. Springer Science & Business Media.
101:It helps me right all stiles of righting,
1319:) is being considered for deletion. See
1272:. googlesystem.blogspot.com. 29 May 2009
1031:Switching to the Mac: The Missing Manual
977:
759:
459:
126:
79:Eye strike the quays and type a whirred
16:Software to help correct spelling errors
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848:
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270:The GNU project has its spell checker
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734:. Stanford University. Archived from
713:Zampieri, M.; de Amorim, R.C. (2014)
693:de Amorim, R.C.; Zampieri, M. (2013)
106:Each frays come posed up on my screen
1842:Simple Knowledge Organization System
448:is more frequently a misspelling of
401:offer spell checking support, using
1171:Friedman, Richard A.; D, M (2003).
785:
729:"The First Three Spelling Checkers"
726:
13:
1127:. e-MedTools. 2017. Archived from
39:that checks for misspellings in a
14:
2158:
1857:Thesaurus (information retrieval)
1323:to help reach a consensus. ›
1302:
1151:"German medical dictionary words"
760:Peterson, James (December 1980).
110:The chequer pours o'er every word
1246:Walt Mossberg (4 January 2007).
1061:. February 21, 1994. p. 68.
964:"Micro Computer Industries, Ltd"
522:Context-sensitive spell checkers
424:
281:, a spell checker that supports
74:It plane lee marks four my revue
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984:Advertisement (November 1982).
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92:Its vary polished in its weigh.
83:Weather eye am write oar wrong
1438:Natural language understanding
1149:Quathamer, Dr. Tobias (2016).
962:Advertisement (January 1981).
842:
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112:Two cheque sum spelling rule.
1:
1962:Optical character recognition
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99:It freeze yew lodes of thyme.
85:It tells me straight a weigh.
1655:Multi-document summarization
655:Perner, Petra (2010-07-05).
502:foreign language writing aid
108:Eye trussed too bee a joule.
90:Your shore real glad two no.
70:Eye have a spelling chequer,
7:
2147:Natural language processing
1985:Latent Dirichlet allocation
1957:Natural language generation
1822:Machine-readable dictionary
1817:Linguistic Linked Open Data
1392:Natural language processing
1073:"Browse September 27, 1993"
599:
388:
322:terminate-and-stay-resident
190:approximate string matching
103:And aides me when eye rime.
97:A chequer is a bless thing,
88:Eye ran this poem threw it,
81:And weight four it two say
76:Miss Steaks I can knot sea.
10:
2163:
1737:Explicit semantic analysis
1486:Deep linguistic processing
1055:"VisualTools VT-Speller".
1034:. "O'Reilly Media, Inc.".
986:"The Spelling Bee Is Over"
220:
211:entries of encyclopedias.
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1998:
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1580:Word-sense disambiguation
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1433:Computational linguistics
1398:
1270:"Google Operating System"
1219:. SpringerLink: 107–130.
537:in the same paragraph as
225:
138:
94:My chequer tolled me sew.
2106:Natural Language Toolkit
2030:Pronunciation assessment
1932:Automatic identification
1762:Latent semantic analysis
1718:Distributional semantics
1603:Compound-term processing
1501:Named-entity recognition
1321:templates for discussion
789:Visible Legacies for Y3K
72:It came with my Pea Sea.
2010:Automated essay scoring
1980:Document classification
1647:Automatic summarization
1225:10.1023/A:1007545901558
283:agglutinative languages
258:
188:Spell checkers can use
47:or services, such as a
1867:Universal Dependencies
1560:Terminology extraction
1543:Semantic decomposition
1538:Semantic role labeling
1528:Part-of-speech tagging
1496:Information extraction
1481:Coreference resolution
1471:Collocation extraction
1326:List of spell checkers
1153:. Dr. Tobias Quathamer
869:"International Ispell"
616:Record linkage problem
496:errors) and will flag
473:
306:
135:
1628:Sentence segmentation
1250:. Wall Street Journal
945:, AbiWord, 2023-02-13
855:Brown Alumni Magazine
681:U.S. Patent 6618697,
584:Microsoft Office 2007
463:
393:Web browsers such as
247:Georgetown University
215:Clustering algorithms
130:
2137:Text editor features
2080:Voice user interface
1791:datasets and corpora
1732:Document-term matrix
1585:Word-sense induction
1028:David Pogue (2015).
1013:David Pogue (2009).
343:and eventually even
265:International Ispell
194:Levenshtein distance
2060:Interactive fiction
1990:Pachinko allocation
1947:Speech segmentation
1903:Google Ngram Viewer
1675:Machine translation
1665:Text simplification
1660:Sentence extraction
1548:Semantic similarity
621:Spelling suggestion
192:algorithms such as
176:synthetic languages
2070:Question answering
1942:Speech recognition
1807:Corpus linguistics
1787:Language resources
1570:Textual entailment
1553:Sentiment analysis
1195:Banks, T. (2008).
1177:The New York Times
921:hunspell.github.io
741:on 22 October 2012
700:2017-08-17 at the
474:
440:corpus linguistics
293:in version 2.0.2.
136:
2119:
2118:
2075:Virtual assistant
2000:Computer-assisted
1926:
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1683:Computer-assisted
1641:
1640:
1633:Word segmentation
1595:Text segmentation
1533:Semantic analysis
1521:Syntactic parsing
1506:Ontology learning
1111:battlepenguin.com
917:"Hunspell: About"
668:978-3-642-14399-1
236:assembly language
2154:
2096:Formal semantics
2045:Natural language
1952:Speech synthesis
1934:and data capture
1837:Semantic network
1812:Lexical resource
1795:
1794:
1613:Lexical analysis
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1590:
1516:Semantic parsing
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1213:Machine Learning
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606:Cupertino effect
594:Grammar checkers
464:A screenshot of
413:, respectively.
37:software feature
29:spelling checker
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2064:Syntax guessing
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2025:Predictive text
2020:Grammar checker
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1888:Bank of English
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1466:Argument mining
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485:since Word 95.
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351:languages like
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25:spell checker
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2015:Concordancer
1411:Bag-of-words
1339:Peter Norvig
1314:
1290:25 September
1288:. Retrieved
1276:25 September
1274:. Retrieved
1264:
1254:24 September
1252:. Retrieved
1241:
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1176:
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1129:the original
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920:
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896:
893:"GNU Aspell"
887:
876:. Retrieved
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832:. Retrieved
828:the original
804:. Retrieved
797:the original
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770:. Retrieved
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736:the original
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636:LanguageTool
626:Words (Unix)
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314:Random House
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164:contractions
142:
69:
53:email client
32:
28:
24:
18:
1972:Topic model
1852:Text corpus
1698:Statistical
1565:Text mining
1406:AI-complete
1350:CBSNews.com
1309:‹ The
1092:PC Magazine
991:PC Magazine
588:Google Wave
490:grammatical
417:Specialties
333:WordPerfect
232:Les Earnest
205:suggestions
168:possessives
151:attributes.
33:spell check
2126:Categories
1693:Rule-based
1575:Truecasing
1443:Stop words
1335:Norvig.com
1182:2018-08-29
1157:2018-08-29
1135:2018-08-29
1077:VT-SPELLER
998:21 October
949:2023-02-19
926:2023-02-19
902:2023-02-19
897:aspell.net
878:2023-02-19
834:2008-12-18
806:2011-02-18
772:2011-02-18
745:10 October
642:References
498:neologisms
411:GNU Aspell
383:VT Speller
368:strategy.
272:GNU Aspell
156:morphology
57:dictionary
2002:reviewing
1800:standards
1798:Types and
1344:BBK.ac.uk
551:homophone
494:homophone
366:marketing
353:Hungarian
242:program.
230:In 1961,
2142:Spelling
1918:Wikidata
1898:FrameNet
1883:BabelNet
1862:Treebank
1832:PropBank
1777:Word2vec
1742:fastText
1623:Stemming
1311:template
1248:"Review"
1233:12283016
698:Archived
600:See also
543:Thailand
403:Hunspell
389:Browsers
374:Mac OS X
341:European
339:to many
329:WordStar
279:Hunspell
209:see also
45:software
21:software
2089:Related
2055:Chatbot
1913:WordNet
1893:DBpedia
1767:Seq2seq
1511:Parsing
1426:Trigram
1313:below (
560:coming
528:context
470:AbiWord
466:Enchant
407:MySpell
395:Firefox
361:Iceland
357:Finnish
337:English
316:rushed
301:AbiWord
297:Enchant
287:MySpell
221:History
198:n-grams
160:English
35:) is a
2062:(c.f.
1720:models
1708:Neural
1421:Bigram
1416:n-gram
1330:Curlie
1316:Curlie
1231:
1038:
665:
580:Winnow
479:Oracle
468:, the
240:ispell
226:Pre-PC
166:, and
139:Design
2111:spaCy
1756:large
1747:GloVe
1229:S2CID
967:(PDF)
800:(PDF)
793:(PDF)
767:(PDF)
739:(PDF)
732:(PDF)
558:Their
431:typos
59:, or
1876:Data
1727:BERT
1292:2010
1278:2010
1256:2010
1036:ISBN
1000:2013
747:2011
663:ISBN
586:and
572:reel
547:bath
539:Thai
534:baht
450:bath
445:baht
409:and
397:and
355:and
331:and
263:The
259:Unix
41:text
27:(or
23:, a
1908:UBY
1328:at
1221:doi
569:its
567:if
565:sea
562:too
541:or
454:bat
452:or
318:OEM
307:PCs
289:in
31:or
19:In
2128::
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Text is available under the Creative Commons Attribution-ShareAlike License. Additional terms may apply.
