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dc.contributor.authorTeahan, W.J.
dc.contributor.authorCleary, John G.
dc.date.accessioned2008-10-22T03:04:43Z
dc.date.available2008-10-22T03:04:43Z
dc.date.issued1997-11
dc.identifier.citationTeahan, W.J. & Cleary, J.G. (1997). Adaptive models of English text. (Working paper 97/30). Hamilton, New Zealand: University of Waikato, Department of Computer Science.en_US
dc.identifier.issn1170-487X
dc.identifier.urihttps://hdl.handle.net/10289/1126
dc.description.abstractHigh quality models of English text with performance approaching that of humans is important for many applications including spelling correction, speech recognition, OCR, and encryption. A number of different statistical models of English are compared with each other and with previous estimates from human subjects. It is concluded that the best current models are word based with part of speech tags. Given sufficient training text, they are able to attain performance comparable to humans.en_US
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherDepartment of Computer Science, The University of Waikato
dc.relation.ispartofseriesComputer Science Working Papers
dc.subjectstatistical language modelingen_US
dc.subjectthe entropy of Englishen_US
dc.subjecttext compressionen_US
dc.subjectadaptive n-gram modelsen_US
dc.subjectPPM text compression schemeen_US
dc.titleAdaptive models of English texten_US
dc.typeWorking Paperen_US
uow.relation.series97/30
pubs.elements-id54712
pubs.place-of-publicationHamiltonen_NZ


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