472:
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
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
139:
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.
528:
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
444:
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.
725:
487:
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
128:
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
467:
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.
161:
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
322:
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
541:
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,
523:
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
382:
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.
374:
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
471:
185:
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.
1331:
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.
1393:
1553:
331:
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
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."
1531:
537:
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
181:. For many other languages, such as those featuring agglutination and more complex declension and conjugation, this part of the process is more complicated.
728:
Proceedings of the 9th
International Conference on Natural Language Processing (PolTAL). Lecture Notes in Computer Science (LNCS). Springer. p. 438-449.
1942:
1386:
669:
Advances in Data Mining: Applications and
Theoretical Aspects: 10th Industrial Conference, ICDM 2010, Berlin, Germany, July 12-14, 2010. Proceedings
440:
The first spell checkers were "verifiers" instead of "correctors." They offered no suggestions for incorrectly spelled words. This was helpful for
2111:
1360:
677:
1183:
2157:
1852:
1543:
1379:
2106:
1713:
338:
However, the market for standalone packages was short-lived, as by the mid-1980s developers of popular word-processing packages like
1867:
1698:
1050:
346:
had incorporated spell checkers in their packages, mostly licensed from the above companies, who quickly expanded support from just
288:
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-
708:
1638:
207:
to find correct spellings of misspelled words. An alternative type of spell checker uses solely statistical information, such as
2055:
1708:
328:
1703:
1448:
739:
2147:
1972:
1693:
1336:
1294:
1280:
1665:
798:
512:
256:
The first spell checkers were widely available on mainframe computers in the late 1970s. A group of six linguists from
2010:
1995:
1967:
1832:
1827:
1402:
861:
332:
285:. Aspell's main improvement is that it can more accurately suggest correct alternatives for misspelled English words.
200:
1135:
693:
1747:
1718:
1496:
1117:
1590:
1443:
218:
for those misspellings; this less flexible approach is often used in paper-based correction methods, such as the
834:
2116:
2040:
1772:
1728:
1613:
1511:
515:
that non-native language learners can rely on to detect and correct their misspellings in the target language.
278:
program commonly used in Unix is based on R. E. Gorin's SPELL. It was converted to C by Pace
Willisson at MIT.
178:
166:
2142:
2020:
1990:
1657:
667:
296:
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,
1877:
1570:
1548:
1538:
1506:
1481:
726:
Between Sound and
Spelling: Combining Phonetics and Clustering Algorithms to Improve Target Word Recovery.
626:
538:
293:
1737:
715:
Proceedings of Recent
Advances in Natural Language Processing (RANLP2013). Hissar, Bulgaria. p. 172-178.
594:
257:
174:
772:
1366:
449:
skipped because they are mistaken for others. For example, a linguist might determine on the basis of
2090:
1766:
1742:
1595:
974:
225:
204:
2070:
2000:
1957:
1913:
1685:
1675:
1670:
1558:
631:
432:
Some spell checkers have separate support for medical dictionaries to help prevent medical errors.
351:
215:
607:
attempt to fix problems with grammar beyond spelling errors, including incorrect choice of words.
2080:
1952:
1817:
1580:
1563:
1421:
1239:
1083:
590:
489:
476:
450:
363:
307:
186:
2152:
2085:
1797:
1605:
1516:
1046:
996:
673:
511:
and foreign words as misspellings. Nonetheless, spell checkers can be considered as a type of
246:
1962:
1847:
1822:
1623:
1526:
1231:
616:
367:
347:
170:
47:
2074:
2035:
2030:
1898:
1628:
1501:
1476:
1458:
1326:
712:
621:
604:
145:
spell checker in action for the above poem, the word "chequer" marked as unrecognized word
1212:. (pp. 29). Master's Thesis, Dominican University of California. Retrieved 19 March 2012.
503:
errors. However, even at their best, they rarely catch all the errors in a text (such as
263:
835:"Georgetown U Faculty & Staff: The Center for Language, Education & Development"
1782:
1762:
1486:
1367:
History and text of "Candidate for a Pullet Surprise" by Mark Eckman and Jerrold H. Zar
705:
641:
493:
355:
301:
193:
130:
59:
2136:
1857:
1837:
1618:
1354:
1321:
1068:
525:
409:
359:
142:
71:
1258:
1243:
387:'s spellcheck coverage includes virtually all bundled and third party applications.
2025:
1349:
646:
636:
324:
196:
allows users to approve or reject replacements and modify the program's operation.
63:
1040:
746:
1982:
1862:
1575:
1491:
1468:
1416:
1102:
1001:
598:
500:
343:
242:
17:
1585:
1371:
1235:
807:
421:
282:
67:
499:
Spell checkers became increasingly sophisticated; now capable of recognizing
1453:
879:
561:
508:
504:
376:
51:
228:
have also been used for spell checking combined with phonetic information.
138:
1161:
1139:
1928:
1908:
1893:
1872:
1842:
1787:
1752:
1633:
413:
384:
339:
335:
mode from within word-processing packages on PCs with sufficient memory.
289:
55:
31:
589:
The most successful algorithm to date is Andrew Golding and Dan Roth's "
2065:
1923:
1903:
1777:
1521:
1436:
1345:
1207:
480:
417:
405:
371:
311:
297:
1431:
1426:
1340:
951:
838:
275:
266:
invented one for the VAX machines of Digital Equipment Corp in 1981.
208:
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
214:
In some cases, spell checkers use a fixed list of misspellings and
2121:
1757:
470:
441:
165:
An additional step is a language-dependent algorithm for handling
137:
1643:
560:. The most common example of errors caught by such a system are
544:
455:
260:
developed the first spell-check system for the IBM corporation.
1375:
1184:"CASES; Do Spelling and Penmanship Count? In Medicine, You Bet"
694:
Method for rule-based correction of spelling and grammar errors
1918:
1209:
Foreign Language Learning Difficulties and Teaching Strategies
927:
774:
Computer Programs for Detecting and Correcting Spelling Errors
706:
Effective Spell Checking Methods Using Clustering Algorithms.
1363:, Spell-Check Crutch Curtails Correctness, by Lloyd de Vries
154:
A basic spell checker carries out the following processes:
564:
errors, such as the bold words in the following sentence:
1222:
Golding, Andrew R.; Roth, Dan (1999). "Journal Article".
862:"Teaching Computers to Spell (obituary for Henry Kučera)"
158:
It scans the text and extracts the words contained in it.
903:
829:
827:
2099:
2054:
2009:
1981:
1941:
1886:
1808:
1796:
1727:
1684:
1656:
1604:
1467:
1409:
1136:"Medical Spell Checker for Firefox and Thunderbird"
1118:"Aspell and Hunspell: A Tale of Two Spell Checkers"
416:. Prior to using Hunspell, Firefox and Chrome used
192:As an adjunct to these components, the program's
189:such as German, Hungarian, or Turkish are clear.
54:. Spell-checking features are often embedded in
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.
1357:, "Spellchecking by computer", by Roger Mitton
1387:
169:. Even for a lightly inflected language like
8:
556:would not be recognized as a misspelling of
1805:
1601:
1394:
1380:
1372:
1348:, "How to Write a Spelling Corrector", by
1295:"Google's Context-Sensitive Spell Checker"
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,
1330:) is being considered for deletion. See
1283:. googlesystem.blogspot.com. 29 May 2009
1042:Switching to the Mac: The Missing Manual
90:Eye strike the quays and type a whirred
27:Software to help correct spelling errors
658:
281:The GNU project has its spell checker
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
7:
1853:Simple Knowledge Organization System
459:is more frequently a misspelling of
412:offer spell checking support, using
1182:Friedman, Richard A.; D, M (2003).
740:"The First Three Spelling Checkers"
1138:. e-MedTools. 2017. Archived from
50:that checks for misspellings in a
25:
1868:Thesaurus (information retrieval)
1334:to help reach a consensus. ›
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
292:, a spell checker that supports
85:It plane lee marks four my revue
995:Advertisement (November 1982).
103:Its vary polished in its weigh.
94:Weather eye am write oar wrong
1449:Natural language understanding
1160:Quathamer, Dr. Tobias (2016).
973:Advertisement (January 1981).
123:Two cheque sum spelling rule.
1:
1973:Optical character recognition
110:It freeze yew lodes of thyme.
96:It tells me straight a weigh.
1666: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,
2158:Natural language processing
1996:Latent Dirichlet allocation
1968:Natural language generation
1833:Machine-readable dictionary
1828:Linguistic Linked Open Data
1403:Natural language processing
1084:"Browse September 27, 1993"
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.
2174:
1748:Explicit semantic analysis
1497:Deep linguistic processing
1066:"VisualTools VT-Speller".
1045:. "O'Reilly Media, Inc.".
997:"The Spelling Bee Is Over"
222:entries of encyclopedias.
1591:Word-sense disambiguation
1444:Computational linguistics
1281:"Google Operating System"
1230:. SpringerLink: 107–130.
548:in the same paragraph as
105:My chequer tolled me sew.
2117:Natural Language Toolkit
2041:Pronunciation assessment
1943:Automatic identification
1773:Latent semantic analysis
1729:Distributional semantics
1614:Compound-term processing
1512:Named-entity recognition
1332:templates for discussion
800:Visible Legacies for Y3K
83:It came with my Pea Sea.
2021:Automated essay scoring
1991:Document classification
1658:Automatic summarization
1236:10.1023/A:1007545901558
294:agglutinative languages
199:Spell checkers can use
58:or services, such as a
1878:Universal Dependencies
1571:Terminology extraction
1554:Semantic decomposition
1549:Semantic role labeling
1539:Part-of-speech tagging
1507:Information extraction
1492:Coreference resolution
1482:Collocation extraction
1337:List of spell checkers
1164:. Dr. Tobias Quathamer
880:"International Ispell"
627:Record linkage problem
507:errors) and will flag
484:
146:
1639: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:
2148:Text editor features
2091:Voice user interface
1802:datasets and corpora
1743:Document-term matrix
1596:Word-sense induction
1039:David Pogue (2015).
1024:David Pogue (2009).
354:and eventually even
276:International Ispell
205:Levenshtein distance
2071:Interactive fiction
2001:Pachinko allocation
1958:Speech segmentation
1914:Google Ngram Viewer
1686:Machine translation
1676:Text simplification
1671:Sentence extraction
1559:Semantic similarity
632:Spelling suggestion
203:algorithms such as
187:synthetic languages
2081:Question answering
1953:Speech recognition
1818:Corpus linguistics
1798:Language resources
1581:Textual entailment
1564: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:
2130:
2129:
2086:Virtual assistant
2011:Computer-assisted
1937:
1936:
1694:Computer-assisted
1652:
1651:
1644:Word segmentation
1606:Text segmentation
1544:Semantic analysis
1532:Syntactic parsing
1517:Ontology learning
1122:battlepenguin.com
928:"Hunspell: About"
679:978-3-642-14399-1
247:assembly language
16:(Redirected from
2165:
2107:Formal semantics
2056:Natural language
1963:Speech synthesis
1945:and data capture
1848:Semantic network
1823:Lexical resource
1806:
1624:Lexical analysis
1602:
1527:Semantic parsing
1396:
1389:
1382:
1373:
1307:
1306:
1304:
1302:
1292:
1290:
1288:
1277:
1271:
1270:
1268:
1266:
1254:
1248:
1247:
1224:Machine Learning
1219:
1213:
1204:
1198:
1197:
1195:
1194:
1179:
1173:
1172:
1170:
1169:
1157:
1151:
1150:
1148:
1147:
1132:
1126:
1125:
1114:
1108:
1107:
1097:
1091:
1090:
1080:
1074:
1073:
1063:
1057:
1056:
1036:
1030:
1029:
1021:
1015:
1014:
1012:
1010:
992:
986:
985:
979:
970:
964:
963:
962:
961:
948:
942:
941:
939:
938:
924:
918:
917:
915:
914:
900:
894:
893:
891:
890:
876:
870:
869:
857:
851:
849:
847:
846:
837:. Archived from
831:
822:
821:
819:
818:
812:
806:. Archived from
805:
794:
788:
787:
785:
784:
779:
768:
762:
761:
759:
757:
751:
744:
735:
729:
722:
716:
702:
696:
690:
684:
683:
663:
617:Cupertino effect
605:Grammar checkers
475:A screenshot of
424:, respectively.
48:software feature
40:spelling checker
21:
18:Spelling checker
2173:
2172:
2168:
2167:
2166:
2164:
2163:
2162:
2133:
2132:
2131:
2126:
2095:
2075:Syntax guessing
2057:
2050:
2036:Predictive text
2031:Grammar checker
2012:
2005:
1977:
1944:
1933:
1899:Bank of English
1882:
1810:
1801:
1792:
1723:
1680:
1648:
1600:
1502:Distant reading
1477:Argument mining
1463:
1459:Text processing
1405:
1400:
1335:
1316:
1311:
1310:
1300:
1298:
1293:
1286:
1284:
1279:
1278:
1274:
1264:
1262:
1256:
1255:
1251:
1221:
1220:
1216:
1205:
1201:
1192:
1190:
1181:
1180:
1176:
1167:
1165:
1159:
1158:
1154:
1145:
1143:
1134:
1133:
1129:
1116:
1115:
1111:
1099:
1098:
1094:
1082:
1081:
1077:
1065:
1064:
1060:
1053:
1038:
1037:
1033:
1023:
1022:
1018:
1008:
1006:
994:
993:
989:
977:
972:
971:
967:
959:
957:
953:AbiWord/enchant
950:
949:
945:
936:
934:
926:
925:
921:
912:
910:
902:
901:
897:
888:
886:
878:
877:
873:
859:
858:
854:
844:
842:
833:
832:
825:
816:
814:
810:
803:
796:
795:
791:
782:
780:
777:
770:
769:
765:
755:
753:
749:
742:
737:
736:
732:
723:
719:
713:Wayback Machine
703:
699:
691:
687:
680:
665:
664:
660:
655:
622:Grammar checker
613:
535:
521:
496:since Word 95.
438:
430:
402:
362:languages like
356:Asian languages
320:
272:
239:
234:
152:
136:
135:
134:
126:
125:
122:
120:
118:
116:
115:
113:
111:
109:
107:
106:
104:
102:
100:
98:
97:
95:
93:
91:
89:
88:
86:
84:
82:
28:
23:
22:
15:
12:
11:
5:
2171:
2169:
2161:
2160:
2155:
2150:
2145:
2143:Spell checkers
2135:
2134:
2128:
2127:
2125:
2124:
2119:
2114:
2109:
2103:
2101:
2097:
2096:
2094:
2093:
2088:
2083:
2078:
2068:
2062:
2060:
2058:user interface
2052:
2051:
2049:
2048:
2043:
2038:
2033:
2028:
2023:
2017:
2015:
2007:
2006:
2004:
2003:
1998:
1993:
1987:
1985:
1979:
1978:
1976:
1975:
1970:
1965:
1960:
1955:
1949:
1947:
1939:
1938:
1935:
1934:
1932:
1931:
1926:
1921:
1916:
1911:
1906:
1901:
1896:
1890:
1888:
1884:
1883:
1881:
1880:
1875:
1870:
1865:
1860:
1855:
1850:
1845:
1840:
1835:
1830:
1825:
1820:
1814:
1812:
1803:
1794:
1793:
1791:
1790:
1785:
1783:Word embedding
1780:
1775:
1770:
1763:Language model
1760:
1755:
1750:
1745:
1740:
1734:
1732:
1725:
1724:
1722:
1721:
1716:
1714:Transfer-based
1711:
1706:
1701:
1696:
1690:
1688:
1682:
1681:
1679:
1678:
1673:
1668:
1662:
1660:
1654:
1653:
1650:
1649:
1647:
1646:
1641:
1636:
1631:
1626:
1621:
1616:
1610:
1608:
1599:
1598:
1593:
1588:
1583:
1578:
1573:
1567:
1566:
1561:
1556:
1551:
1546:
1541:
1536:
1535:
1534:
1529:
1519:
1514:
1509:
1504:
1499:
1494:
1489:
1487:Concept mining
1484:
1479:
1473:
1471:
1465:
1464:
1462:
1461:
1456:
1451:
1446:
1441:
1440:
1439:
1434:
1424:
1419:
1413:
1411:
1407:
1406:
1401:
1399:
1398:
1391:
1384:
1376:
1370:
1369:
1364:
1358:
1352:
1343:
1319:
1315:
1314:External links
1312:
1309:
1308:
1297:. May 29, 2009
1272:
1249:
1214:
1199:
1174:
1152:
1127:
1109:
1106:. p. 299.
1092:
1075:
1058:
1051:
1031:
1016:
987:
984:. p. 119.
965:
943:
919:
895:
884:www.cs.hmc.edu
871:
852:
823:
797:Earnest, Les.
789:
763:
738:Earnest, Les.
730:
717:
697:
685:
678:
657:
656:
654:
651:
650:
649:
644:
642:Autocorrection
639:
634:
629:
624:
619:
612:
609:
587:
586:
534:
531:
520:
517:
494:Microsoft Word
453:that the word
437:
434:
429:
426:
401:
398:
319:
316:
302:OpenOffice.org
271:
268:
238:
235:
233:
230:
194:user interface
183:
182:
163:
159:
151:
148:
131:language model
127:
79:
78:
77:
76:
60:word processor
26:
24:
14:
13:
10:
9:
6:
4:
3:
2:
2170:
2159:
2156:
2154:
2151:
2149:
2146:
2144:
2141:
2140:
2138:
2123:
2120:
2118:
2115:
2113:
2112:Hallucination
2110:
2108:
2105:
2104:
2102:
2098:
2092:
2089:
2087:
2084:
2082:
2079:
2076:
2072:
2069:
2067:
2064:
2063:
2061:
2059:
2053:
2047:
2046:Spell checker
2044:
2042:
2039:
2037:
2034:
2032:
2029:
2027:
2024:
2022:
2019:
2018:
2016:
2014:
2008:
2002:
1999:
1997:
1994:
1992:
1989:
1988:
1986:
1984:
1980:
1974:
1971:
1969:
1966:
1964:
1961:
1959:
1956:
1954:
1951:
1950:
1948:
1946:
1940:
1930:
1927:
1925:
1922:
1920:
1917:
1915:
1912:
1910:
1907:
1905:
1902:
1900:
1897:
1895:
1892:
1891:
1889:
1885:
1879:
1876:
1874:
1871:
1869:
1866:
1864:
1861:
1859:
1858:Speech corpus
1856:
1854:
1851:
1849:
1846:
1844:
1841:
1839:
1838:Parallel text
1836:
1834:
1831:
1829:
1826:
1824:
1821:
1819:
1816:
1815:
1813:
1807:
1804:
1799:
1795:
1789:
1786:
1784:
1781:
1779:
1776:
1774:
1771:
1768:
1764:
1761:
1759:
1756:
1754:
1751:
1749:
1746:
1744:
1741:
1739:
1736:
1735:
1733:
1730:
1726:
1720:
1717:
1715:
1712:
1710:
1707:
1705:
1702:
1700:
1699:Example-based
1697:
1695:
1692:
1691:
1689:
1687:
1683:
1677:
1674:
1672:
1669:
1667:
1664:
1663:
1661:
1659:
1655:
1645:
1642:
1640:
1637:
1635:
1632:
1630:
1629:Text chunking
1627:
1625:
1622:
1620:
1619:Lemmatisation
1617:
1615:
1612:
1611:
1609:
1607:
1603:
1597:
1594:
1592:
1589:
1587:
1584:
1582:
1579:
1577:
1574:
1572:
1569:
1568:
1565:
1562:
1560:
1557:
1555:
1552:
1550:
1547:
1545:
1542:
1540:
1537:
1533:
1530:
1528:
1525:
1524:
1523:
1520:
1518:
1515:
1513:
1510:
1508:
1505:
1503:
1500:
1498:
1495:
1493:
1490:
1488:
1485:
1483:
1480:
1478:
1475:
1474:
1472:
1470:
1469:Text analysis
1466:
1460:
1457:
1455:
1452:
1450:
1447:
1445:
1442:
1438:
1435:
1433:
1430:
1429:
1428:
1425:
1423:
1420:
1418:
1415:
1414:
1412:
1410:General terms
1408:
1404:
1397:
1392:
1390:
1385:
1383:
1378:
1377:
1374:
1368:
1365:
1362:
1359:
1356:
1353:
1351:
1347:
1344:
1342:
1338:
1333:
1329:
1328:
1323:
1318:
1317:
1313:
1296:
1282:
1276:
1273:
1260:
1253:
1250:
1245:
1241:
1237:
1233:
1229:
1225:
1218:
1215:
1211:
1210:
1203:
1200:
1189:
1185:
1178:
1175:
1163:
1156:
1153:
1142:on 2019-05-04
1141:
1137:
1131:
1128:
1123:
1119:
1113:
1110:
1105:
1104:
1096:
1093:
1089:
1085:
1079:
1076:
1071:
1070:
1069:Computerworld
1062:
1059:
1054:
1052:9781491948125
1048:
1044:
1043:
1035:
1032:
1027:
1020:
1017:
1005:. p. 165
1004:
1003:
998:
991:
988:
983:
976:
969:
966:
955:
954:
947:
944:
933:
929:
923:
920:
909:
905:
899:
896:
885:
881:
875:
872:
868:. p. 79.
867:
863:
856:
853:
841:on 2009-02-05
840:
836:
830:
828:
824:
813:on 2011-07-20
809:
802:
801:
793:
790:
776:
775:
767:
764:
748:
741:
734:
731:
727:
721:
718:
714:
710:
707:
701:
698:
695:
689:
686:
681:
675:
671:
670:
662:
659:
652:
648:
645:
643:
640:
638:
635:
633:
630:
628:
625:
623:
620:
618:
615:
614:
610:
608:
606:
602:
600:
596:
592:
584:
581:
577:
574:
570:
567:
566:
565:
563:
559:
555:
551:
547:
546:
540:
532:
530:
527:
518:
516:
514:
510:
506:
502:
497:
495:
491:
483:spell checker
482:
478:
473:
469:
466:
462:
458:
457:
452:
446:
443:
436:Functionality
435:
433:
427:
425:
423:
419:
415:
411:
410:Google Chrome
407:
399:
397:
395:
392:
391:Visual Tools'
388:
386:
380:
378:
373:
369:
365:
361:
360:agglutinative
357:
353:
349:
345:
341:
336:
334:
330:
326:
317:
315:
313:
309:
305:
303:
299:
295:
291:
286:
284:
279:
277:
269:
267:
265:
261:
259:
254:
252:
248:
244:
236:
231:
229:
227:
223:
221:
217:
212:
210:
206:
202:
197:
195:
190:
188:
180:
176:
172:
168:
164:
160:
157:
156:
155:
149:
144:
143:Google Chrome
140:
132:
124:
75:
73:
72:search engine
69:
66:, electronic
65:
61:
57:
53:
49:
45:
41:
37:
36:spell checker
33:
19:
2045:
2026:Concordancer
1422:Bag-of-words
1350:Peter Norvig
1325:
1301:25 September
1299:. Retrieved
1287:25 September
1285:. Retrieved
1275:
1265:24 September
1263:. Retrieved
1252:
1227:
1223:
1217:
1208:
1202:
1191:. Retrieved
1187:
1177:
1166:. Retrieved
1155:
1144:. Retrieved
1140:the original
1130:
1121:
1112:
1101:
1095:
1087:
1078:
1067:
1061:
1041:
1034:
1025:
1019:
1007:. Retrieved
1000:
990:
981:
968:
958:, retrieved
952:
946:
935:. Retrieved
931:
922:
911:. Retrieved
907:
904:"GNU Aspell"
898:
887:. Retrieved
883:
874:
865:
855:
843:. Retrieved
839:the original
815:. Retrieved
808:the original
799:
792:
781:. Retrieved
773:
766:
754:. Retrieved
747:the original
733:
720:
700:
688:
668:
661:
647:LanguageTool
637:Words (Unix)
603:
588:
582:
579:
575:
572:
568:
557:
553:
549:
543:
536:
526:concatenated
522:
498:
486:
464:
460:
454:
447:
439:
431:
403:
393:
390:
389:
381:
337:
325:Random House
321:
306:
287:
280:
273:
264:Henry Kučera
262:
255:
250:
240:
224:
219:
213:
198:
191:
184:
175:contractions
153:
80:
64:email client
43:
39:
35:
29:
1983:Topic model
1863:Text corpus
1709:Statistical
1576:Text mining
1417:AI-complete
1361:CBSNews.com
1320:‹ The
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
2137:Categories
1704:Rule-based
1586:Truecasing
1454:Stop words
1346: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
2013:reviewing
1811:standards
1809:Types and
1355:BBK.ac.uk
562:homophone
505:homophone
377:marketing
364:Hungarian
253:program.
241:In 1961,
2153:Spelling
1929:Wikidata
1909:FrameNet
1894:BabelNet
1873:Treebank
1843:PropBank
1788:Word2vec
1753:fastText
1634:Stemming
1322:template
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
2100:Related
2066:Chatbot
1924:WordNet
1904:DBpedia
1778:Seq2seq
1522:Parsing
1437:Trigram
1324:below (
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
2073:(c.f.
1731:models
1719:Neural
1432:Bigram
1427:n-gram
1341:Curlie
1327:Curlie
1242:
1049:
676:
591:Winnow
490:Oracle
479:, the
251:ispell
237:Pre-PC
177:, and
150:Design
2122:spaCy
1767:large
1758:GloVe
1240:S2CID
978:(PDF)
811:(PDF)
804:(PDF)
778:(PDF)
750:(PDF)
743:(PDF)
569:Their
442:typos
70:, or
1887:Data
1738: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
1919:UBY
1339: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
2139::
1238:.
1228:34
1226:.
1186:.
1120:.
1086:.
999:.
980:.
930:.
906:.
882:.
864:.
826:^
601:.
74:.
62:,
2077:)
1800:,
1769:)
1765:(
1395:e
1388:t
1381:v
1305:.
1291:.
1269:.
1246:.
1234::
1196:.
1171:.
1149:.
1124:.
1055:.
1028:.
1013:.
940:.
916:.
892:.
848:.
820:.
786:.
760:.
682:.
585:.
20:)
Text is available under the Creative Commons Attribution-ShareAlike License. Additional terms may apply.
