Better text compression from fewer lexical n-grams

Smith, Tony C.; Lorenz, Michelle

Better text compression from fewer lexical n-grams

dc.contributor.author	Smith, Tony C.
dc.contributor.author	Lorenz, Michelle
dc.coverage.spatial	Conference held at Snowbird, Utah	en_NZ
dc.date.accessioned	2008-12-18T20:53:34Z
dc.date.available	2008-12-18T20:53:34Z
dc.date.issued	2001
dc.description.abstract	Word-based context models for text compression have the capacity to outperform more simple character-based models, but are generally unattractive because of inherent problems with exponential model growth and corresponding data sparseness. These ill-effects can be mitigated in an adaptive lossless compression scheme by modelling syntactic and semantic lexical dependencies independently.	en
dc.format.mimetype	application/pdf
dc.identifier.citation	Smith, T.C. & Lorenz, M.(2001). Better text compression from fewer lexical n-grams. In Proceedings of Data Compression Conference (DCC ‘01). Washington, DC, USA: IEEE Computer Society.	en
dc.identifier.doi	10.1109/DCC.2001.10047	en
dc.identifier.uri	https://hdl.handle.net/10289/1722
dc.language.iso	en
dc.publisher	IEEE Computer Society	en
dc.relation.isPartOf	DCC 2001: IEEE Data Compression Conference	en_NZ
dc.rights	This paper has been published in the Proceedings of Data Compression Conference(DCC ‘01). ©2001 IEEE Computer Society.	en
dc.subject	computer science	en
dc.subject	text compression	en
dc.subject	Machine learning
dc.title	Better text compression from fewer lexical n-grams	en
dc.type	Conference Contribution	en
pubs.begin-page	516	en_NZ
pubs.elements-id	11594
pubs.end-page	516	en_NZ
pubs.finish-date	2001-03-29	en_NZ
pubs.start-date	2001-03-27	en_NZ