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the head and in frontal attack on an english writer that the character of this point is therefore another method for the letters that the time of who ever told the problem for an unexpected
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such as "monomer", "dimer", "trimer", "tetramer", "pentamer", etc., or
English cardinal numbers, "one-mer", "two-mer", "three-mer", etc. are used in computational biology, for
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Here are further examples; these are word-level 3-grams and 4-grams (and counts of the number of times they appeared) from the Google
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models to capture information such as word order, which would not be possible in the traditional bag of words setting.
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Broder, Andrei Z.; Glassman, Steven C.; Manasse, Mark S.; Zweig, Geoffrey (1997). "Syntactic clustering of the web".
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extracted from a speech-recording dataset, or adjacent base pairs extracted from a genome. They are collected from a
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43:-grams frequently found in titles of publications about Coronavirus disease 2019 (COVID-19), as of 7 May 2020
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in no ist lat whey cratict froure birs grocid pondenome of demonstures of the retagin is regiactiona of cre
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Figure 1 shows several example sequences and the corresponding 1-gram, 2-gram and 3-gram sequences.
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White, Owen; Dunning, Ted; Sutton, Granger; Adams, Mark; Venter, J. Craig; Fields, Chris (1993).
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604:. IEEE International Conference on Computer, Information and Telecommunication Systems (CITS).
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2-gram word model (random draw of words taking into account their transition probabilities):
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567:"Contextual Language Models For Ranking Answers To Natural Language Definition Questions"
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are furtherly used, then they are called "four-gram", "five-gram", etc. Similarly, using
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Cybernetics; Transactions of the 7th
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3-gram character model (random draw based on the probabilities of each trigram):
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Brocardo, Marcelo Luiz; Traore, Issa; Saad, Sherif; Woungang, Isaac (2013).
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adjacent symbols in particular order. The symbols may be
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98:-gram of size 1 is called a "unigram", size 2 a "
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448:Shannon, Claude E. "The redundancy of English."
265:..., Cys-Gly-Leu, Gly-Leu-Ser, Leu-Ser-Trp, ...
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659:-gram viewer for every domain in Alexa Top 1M
338:..., to be, be or, or not, not to, to be, ...
565:Figueroa, Alejandro; Atkinson, John (2012).
262:..., Cys-Gly, Gly-Leu, Leu-Ser, Ser-Trp, ...
500:Manning, Christopher D.; Schütze, Hinrich;
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16:Item sequences in computational linguistics
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78:found in a language dataset; or adjacent
182:-gram examples from various disciplines
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668:Corpus of Contemporary American English
361:ceramics collectables collectibles (55)
289:..., AGC, GCT, CTT, TTC, TCG, CGA, ...
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655:STATOPERATOR N-grams Project Weighted
462:Franz, Alex; Brants, Thorsten (2006).
160:-gram models of English. For example:
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664:1,000,000 most frequent 2,3,4,5-grams
1183:Simple Knowledge Organization System
478:from the original on 17 October 2006
286:..., AG, GC, CT, TT, TC, CG, GA, ...
559:Markov Models and Linguistic Theory
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416:Computer Networks and ISDN Systems
373:ceramics collectibles cooking (45)
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1198:Thesaurus (information retrieval)
628:Ngram Extractor: Gives weight of
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370:ceramics collectible pottery (50)
335:..., to, be, or, not, to, be, ...
259:..., Cys, Gly, Leu, Ser, Trp, ...
583:10.1111/j.1467-8640.2012.00426.x
364:ceramics collectables fine (130)
283:..., A, G, C, T, T, C, G, A, ...
697:OpenRefine: Clustering In Depth
632:-gram based on their frequency.
779:Natural language understanding
673:Peachnote's music ngram viewer
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387:serve as the independent (794)
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1303:Optical character recognition
428:10.1016/s0169-7552(97)00031-7
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996:Multi-document summarization
678:Stochastic Language Models (
396:serve as the indicator (120)
393:serve as the indication (72)
125:. When the items are words,
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1473:Natural language processing
1326:Latent Dirichlet allocation
1298:Natural language generation
1163:Machine-readable dictionary
1158:Linguistic Linked Open Data
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688:Michael Collins's notes on
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384:serve as the incubator (99)
256:... Cys-Gly-Leu-Ser-Trp ...
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138:Natural language processing
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1078:Explicit semantic analysis
827:Deep linguistic processing
666:from the 425 million word
571:Computational Intelligence
381:serve as the incoming (92)
367:ceramics collected by (52)
332:... to be or not to be ...
129:-grams may also be called
30:word n-gram language model
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156:(Shannon 1951) discussed
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1371:Pronunciation assessment
1273:Automatic identification
1103:Latent semantic analysis
1059:Distributional semantics
944:Compound-term processing
842:Named-entity recognition
468:-gram are Belong to You"
390:serve as the index (223)
304:...to_be_or_not_to_be...
118:of a known size, called
108:Greek numerical prefixes
104:English cardinal numbers
92:Latin numerical prefixes
28:Not to be confused with
1351:Automated essay scoring
1321:Document classification
988:Automatic summarization
557:Damerau, Frederick J.;
23:N-gram (disambiguation)
1208:Universal Dependencies
901:Terminology extraction
884:Semantic decomposition
879:Semantic role labeling
869:Part-of-speech tagging
837:Information extraction
822:Coreference resolution
812:Collocation extraction
637:Google's Google Books
533:10.1093/nar/21.16.3829
521:Nucleic Acids Research
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969:Sentence segmentation
692:-Gram Language Models
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1498:Probabilistic models
1421:Voice user interface
1132:datasets and corpora
1073:Document-term matrix
926:Word-sense induction
682:-Gram) Specification
472:Google Research Blog
323:-gram language model
21:For other uses, see
1401:Interactive fiction
1331:Pachinko allocation
1288:Speech segmentation
1244:Google Ngram Viewer
1016:Machine translation
1006:Text simplification
1001:Sentence extraction
889:Semantic similarity
504:, MIT Press: 1999,
225:Order of resulting
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1493:Corpus linguistics
1488:Speech recognition
1411:Question answering
1283:Speech recognition
1148:Corpus linguistics
1128:Language resources
911:Textual entailment
894:Sentiment analysis
247:Protein sequencing
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140:(NLP), the use of
136:In the context of
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1341:Computer-assisted
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1024:Computer-assisted
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974:Word segmentation
936:Text segmentation
874:Semantic analysis
862:Syntactic parsing
847:Ontology learning
527:(16): 3829–3838.
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68:punctuation marks
54:is a sequence of
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1437:Formal semantics
1386:Natural language
1293:Speech synthesis
1275:and data capture
1178:Semantic network
1153:Lexical resource
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1039:Statistical
906:Text mining
747:AI-complete
482:16 December
84:text corpus
66:(including
1467:Categories
1034:Rule-based
916:Truecasing
784:Stop words
401:References
252:amino acid
1343:reviewing
1141:standards
1139:Types and
464:"All Our
300:character
276:base pair
178:Figure 1
116:oligomers
72:syllables
62:adjacent
1259:Wikidata
1239:FrameNet
1224:BabelNet
1203:Treebank
1173:PropBank
1118:Word2vec
1083:fastText
964:Stemming
610:See also
591:27378409
476:Archived
377:4-grams
357:3-grams
220:trigram
152:Examples
131:shingles
112:polymers
80:phonemes
1430:Related
1396:Chatbot
1254:WordNet
1234:DBpedia
1108:Seq2seq
852:Parsing
767:Trigram
551:8367301
452:. 1951.
436:9022773
214:unigram
64:letters
1403:(c.f.
1061:models
1049:Neural
762:Bigram
757:n-gram
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542:309901
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217:bigram
100:bigram
90:. If
1452:spaCy
1097:large
1088:GloVe
684:(W3C)
587:S2CID
432:S2CID
319:Word
187:Field
123:-mers
76:words
52:-gram
1217:Data
1068:BERT
645:Web
643:and
547:PMID
506:ISBN
484:2011
328:word
190:Unit
39:Six
1249:UBY
579:doi
537:PMC
529:doi
424:doi
114:or
86:or
47:An
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466:N
438:.
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352:n
321:n
238:1
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180:n
158:n
142:n
127:n
121:k
96:n
60:n
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41:n
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