Semantic and generative models for lossy text compression

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dc.contributor.authorWitten, Ian H.en_NZ
dc.contributor.authorBell, Timothy C.en_NZ
dc.contributor.authorMoffat, Alistairen_NZ
dc.contributor.authorSmith, Tony C.en_NZ
dc.contributor.authorNevill-Manning, Craig G.en_NZ
dc.date.accessioned2016-02-17T03:48:59Z
dc.date.available1992en_NZ
dc.date.available2016-02-17T03:48:59Z
dc.date.issued1992en_NZ
dc.description.abstractThe apparent divergence between the research paradigms of text and image compression has led us to consider the potential for applying methods developed for one domain to the other. This paper examines the idea of "lossy" text compression, which transmits an approximation to the input text rather than the text itself. In image coding, lossy techniques have proven to yield compression factors that are vastly superior to those of the best lossless schemes, and we show that this a also the case for text. Two different methods are described here, one inspired by the use of fractals in image compression. They can be combined into an extremely effective technique that provides much better compression than the present state of the art and yet preserves a reasonable degree of match between the original and received text. The major challenge for lossy text compression is identified as the reliable evaluation of the quality of this match.en_NZ
dc.format.mimetypeapplication/pdf
dc.identifier.citationWitten, I. H., Bell, T. C., Moffat, A., Smith, T. C., & Nevill-Manning, C. G. (1992). Semantic and generative models for lossy text compression (Computer Science Working Papers 92/8). Hamilton, New Zealand: Department of Computer Science, University of Waikato.en
dc.identifier.issn1170-487Xen_NZ
dc.identifier.urihttps://hdl.handle.net/10289/9911
dc.language.isoen
dc.publisherDepartment of Computer Science, University of Waikatoen_NZ
dc.relation.isPartOfWorking Paper Seriesen_NZ
dc.relation.ispartofseriesComputer Science Working Papers
dc.rights© 1992 by Ian H. Witten, Timothy C. Bell, Alistair Moffat, Tony C. Smith & Craig G. Nevill-Manning.
dc.subjectMachine learning
dc.titleSemantic and generative models for lossy text compressionen_NZ
dc.typeWorking Paper
pubs.confidentialfalseen_NZ
pubs.elements-id137069
pubs.organisational-group/Waikato
pubs.organisational-group/Waikato/FCMS
pubs.organisational-group/Waikato/FCMS/Computer Science
pubs.organisational-group/Waikato/FCMS/Computer Science/ML Group
pubs.place-of-publicationHamilton, New Zealand
uow.relation.series92/8
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